Journal of Civil Engineering and Management
type: channel
description: The Journal of Civil Engineering and Management publishes original research that seeks to improve civil engineering competency, efficiency and productivity in world markets. More information ...
publisher: Vilnius Gediminas Technical University
language: en
publication name: Journal of Civil Engineering and Management
issn: 1392-3730
copyright: Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
items: Autonomous modular construction strategy using robotized crane based on deep learning and reinforcement learning
items: Identifying cyber risk factors associated with construction projects
items: The digital organization transformation (DOT) model: bridging digital transformation and organizational structures in construction firms
items: A systematic review on safety practices in high-rise building construction: benefits, barriers, and strategic improvements
items: Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
items: Prediction of cost contingency in construction projects by introducing machine learning algorithms
items: Collaborative analysis model of construction schedule and quality for RCC dam based on DES and deep learning
items: Advancing civil infrastructure with digital twins: a review of applications and challenges
items: Quotation impact factors for general contractors: a viewpoint from private sectors in Taiwan
items: Evaluation model of construction quality data uncertainty: a study based on industry park project
items: Barriers and facilitators of promoting work-related injury insurance in the construction industry: lessons from China
items: Performance comparison of various time-series forecasting models for bridge sufficiency rating prediction
items: BIM-based digital twin for the management of a railway station
items: Evaluation of the effect of fineness of fly ash in type I and type II Portland cement blended mortars
items: Development of an automated surface crack detection and BIM-integrated management system for concrete bridges
items: Prediction of sewage pipeline construction duration by introducing machine learning and deep learning approaches
items: Exploring the use of in-house sodium silicate from agro-industrial by-products in pervious geopolymer concrete
items: Integration of BIM and ar with VSLAM to assist in construction site inspection
items: Sustainable design of recycled concrete using shape optimization and carbon dioxide emission based on LCA
items: Multi-Criteria Decision Methods in the evaluation of social housing projects
items: Modelling progressive failure of segmental linings in shield tunnels
items: How emotional intelligence affects inter-organizational relationships in megaprojects: the boundary spanning perspective
items: Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
items: Is contractual flexibility always good for relationship quality among the parties in construction projects?
items: Mapping publications on value creation in construction project settings: a mixed bibliographic and bibliometric analysis
items: Sub-activity analysis by using wristband-type wearable health devices to measure construction workers’ physical demands
items: Methodology for assessing and controlling the risk grade of structural columns threatened by blast incident
items: Integrating LINE BOT and Building Information Model to develop construction information management system
items: Measurement and spatiotemporal evolution characteristics analysis for the provincial development level of intelligent construction in China
items: A relative measurement and monitoring of OTTV of glass facade passive building in tropical climate

Autonomous modular construction strategy using robotized crane based on deep learning and reinforcement learning
type: item
description: Modular construction offers significant advantages including faster construction time, higher quality control and less environmental impact. To further enhance its advantages, advanced robotic construction technologies are being developed. This research develops an automated modular construction framework that incorporates the robotic kinematics, deep learning and deep reinforcement learning using a robotized crane. The proposed modular construction strategy utilizes YOLOv5-S for modular container identification and localization. An improved proximal policy optimization (PPO-I) is developed and implemented in this strategy for collision-free three-dimensional (3D) lifting path planning and modular container transportation. States and rewards of the PPO-I and robot kinematics design of a real mobile crane are developed. The feasibility of the proposed modular construction strategy is verified through four case studies in 3D virtual environments. More than 97% success rate is observed meaning that the proposed strategy can be implemented in the robotized crane to localize the modular container and transport it to the target position with collision avoidance. The results indicate the potential of the proposed robotic-assisted modular construction strategy in the field of automated construction.
subject: Articles
  topic: section
subject: modular construction
  topic: keywords
subject: mobile crane
  topic: keywords
subject: robotized crane
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: 3D path planning
  topic: keywords
subject: collision avoidance
  topic: keywords
creator: Yifei Xiao
creator: T. Y. Yang
creator: Xiao Pan
creator: Fan Xie
creator: Amir Ghahremani Baghmisheh
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
starting page: 955
ending page: 972
doi: 10.3846/jcem.2025.24043

Identifying cyber risk factors associated with construction projects
type: item
description: As construction projects adopt increasingly interconnected digital technologies, their cyber-attack surface expands, making comprehensive cyber risk management essential to prevent incidents, mitigate risks, and minimize potential losses resulting from such attacks. However, the necessary risk factors for this purpose are lacking. Therefore, the study aims to develop a comprehensive set of project-level cyber risk factors tailored to the complexities of construction projects, identified through a systematic and flexible seven-step methodological framework: (1) a literature review of construction and cybersecurity sources to identify initial factors; (2) initial definition of risk categories; (3) internal evaluation and expert input to refine these factors; (4) distribution of a detailed expert questionnaire for rating; (5) expert evaluations through meetings and feedback sessions to enhance validity; (6) elimination of lower-scoring factors; and (7) establishment of quantitative scales for precise risk assessment. The findings include the 32 identified risk factors into five groups: project information, project structure, information technology (IT), operational technology (OT), and management and human aspects. The contributions include providing a set of risk factors that serve as cybersecurity management references and inputs for future quantitative risk assessments, offering a checklist used for proactive risk management, and introducing a framework adaptable for identifying factors of other risks.
subject: Articles
  topic: section
subject: construction cybersecurity
  topic: keywords
subject: digital transformation
  topic: keywords
subject: risk factors
  topic: keywords
subject: risk assessment
  topic: keywords
subject: industry-specific vulnerabilities
  topic: keywords
creator: Dongchi Yao
creator: Borja García de Soto
creator: Mike Wilkes
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 926
ending page: 954
doi: 10.3846/jcem.2025.25213

The digital organization transformation (DOT) model: bridging digital transformation and organizational structures in construction firms
type: item
description: Digital transformation is a pathway to improve productivity in the construction sector, but organizational structures still need more adaptation. There are no fully developed models at the level of factors and evidence between organizational structures and digital transformation. This paper analyzes the impact of digital transformation on organizational structures within the construction sector, utilizing a semi-structured interview and field inspection methodology to validate key organizational factors and evidence influencing this process. The research focuses on Colombia’s medium and large construction companies and aims to validate an integrated conceptual model of digital transformation and organizational structures. Results indicate that while technological tools have been adopted, the full potential of digital transformation still needs to be explored. The study highlights the factors and evidence that affect the variables of digital transformation and organizational structures. While technological tools are essential, they are unlikely to create a long-term competitive advantage. This research advances the understanding that the integrated implementation of digital transformation and organizational structures, grounded in validated factors and evidence, can drive enhanced decision-making, reduce inefficiencies, and improve productivity within the construction industry.
subject: Articles
  topic: section
subject: digital transformation
  topic: keywords
subject: organizational structure
  topic: keywords
subject: organizational culture
  topic: keywords
subject: construction sector
  topic: keywords
subject: conceptual model
  topic: keywords
subject: construction firms
  topic: keywords
creator: Cristian C. Osorio-Gómez
creator: Rodrigo F. Herrera
creator: Laura Tupėnaitė
creator: Eugenio Pellicer
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 909
ending page: 925
doi: 10.3846/jcem.2025.24318

A systematic review on safety practices in high-rise building construction: benefits, barriers, and strategic improvements
type: item
description: High-rise building construction has long been considered one of the most dangerous jobs globally. However, there is currently a lack of comprehensive systematic reviews that approach the implementation of safety practices in high-rise construction. This study aims to fill the following research gaps: first, to understand the significance and identify the barriers faced to implementing safety practices in high-rise construction; second, to propose a framework to improve safety performance. A total of 109 articles from databases were thoroughly reviewed in this study. The research identified six categories of benefits, five types of safety barriers, and 15 strategies proposed to overcome these barriers. In terms of the number of sources, “reduced accident rates” and “improved organizational reputation” were identified as the top benefits in terms of the significance of safety practices. “Lack of health and safety training” was deemed the largest barrier. To address these challenges, this paper explores the strategies and risk mitigation measures from the perspectives of six stakeholders: regulatory agencies, government, owners, contractors, consultants, and workers. The findings suggest that establishing strict market entry mechanisms are fundamental while strengthening worker safety training and conducting regular safety inspections and real-time site monitoring are the most effective methods.
subject: Articles
  topic: section
subject: high-rise building
  topic: keywords
subject: benefits
  topic: keywords
subject: barriers
  topic: keywords
subject: construction safety practice
  topic: keywords
subject: stakeholders
  topic: keywords
subject: strategies
  topic: keywords
creator: Wei Rui Lei
creator: Muhamad Azry Khoiry
creator: Azrul A Mutalib
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 893
ending page: 908
doi: 10.3846/jcem.2025.24790

Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
type: item
description: Operation and Maintenance (O&M) aims to preserve the quality of the building throughout its life, keeping maintenance costs within acceptable limits. Maintenance involves different tasks, from replacing air conditioning filters to restoring structural elements. Each task has an optimal frequency, which can be flexible within a specific time range, a cost, and a duration. These maintenance activities may disrupt building operations by repeatedly interrupting ongoing activities. This research seeks to reduce these disruptions by grouping tasks within reasonably close time frames to schedule preventive maintenance plans while respecting their frequency. We propose an optimisation model, solvable using a general-purpose solver, which identifies the best time range for grouping O&M tasks. By penalising deviations from the optimal period, the model ensures that tasks are performed at the most cost-effective time. Integrated within a microservice-based architecture, the optimisation engine seamlessly links an input database and a BIM model, orchestrated using Dynamo for Revit. A case study illustrates the effectiveness of this system, consolidating multiple tasks into optimised work clusters and significantly reducing operational disruptions. The originality of this work lies in its innovative combination of optimisation techniques and BIM tools, providing a practical and scalable solution for efficient O&M management.
subject: Articles
  topic: section
subject: maintenance
  topic: keywords
subject: architecture
  topic: keywords
subject: project management
  topic: keywords
subject: O&M
  topic: keywords
subject: BIM
  topic: keywords
subject: microservice
  topic: keywords
subject: scheduling
  topic: keywords
subject: optimisation
  topic: keywords
subject: Integer Linear Programming
  topic: keywords
creator: Edoardo De Santis
creator: Francesco Livio Rossini
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-13
publication date: 2025-11-13
starting page: 881
ending page: 892
doi: 10.3846/jcem.2025.24957

Prediction of cost contingency in construction projects by introducing machine learning algorithms
type: item
description: Construction projects are bound by uncertainties and changes by its nature. Thus, cost contingency needs to be allocated to construction project budget to cope with any deviation of actual costs from planned ones. However, existing methods for predicting cost contingencies, as studied and practiced, still present limitations in reliability and accuracy. Machine learning (ML) has gained popularity for enhancing prediction power in various fields. The paper aims to examine various ML algorithms to implement a cost contingency prediction model, employing both continuous and categorical predictor variables. To develop the model, construction transportation project datasets, which were bid between 2013‒2017, were collected from the Florida Department of Transportation (FDOT) website. To address imbalanced regression dataset issues, the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) algorithm is introduced. ML random forest (RF) regression associated with random search hyperparameter optimization, achieved remarkably accurate predictions compared to extreme gradient boosting (XGBoost) regression and artificial neural network (ANN) models. The results also demonstrate that four parameters are significant factors in predicting construction cost contingency: project amount, project duration, and latitude and longitude factors. These findings provide new insights for researchers in developing models and for practitioners seeking more advanced method.
subject: Articles
  topic: section
subject: construction cost contingency
  topic: keywords
subject: machine learning
  topic: keywords
subject: RF
  topic: keywords
subject: XGBoost
  topic: keywords
subject: hyperparameter optimization
  topic: keywords
subject: SMOGN
  topic: keywords
subject: cost prediction
  topic: keywords
creator: Acinia Nindartin
creator: Sang-Jun Park
creator: Kyung-Tae Lee
creator: Ju-Hyung Kim
creator: Susy Fatena Rostiyanti
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-13
publication date: 2025-11-13
starting page: 860
ending page: 880
doi: 10.3846/jcem.2025.24913

Collaborative analysis model of construction schedule and quality for RCC dam based on DES and deep learning
type: item
description: Construction schedule simulation is an effective method to analyze the progress of concrete rolling construction and its simulation accuracy is an important indicator that project managers care about. However, existing roller compacter concrete construction simulation research pays less attention to the impact of construction process changes caused by compaction quality on simulation accuracy. Based on the real-time monitoring system of concrete rolling construction, this paper establishes a simulation model of concrete rolling construction considering compaction quality. The model firstly reconstructs the property parameters of roller compacted concrete using the improved generated adversarial network, secondly, considering the nonlinear correlation characteristics of parameters affecting compaction quality, a concrete compaction quality analysis model based on improving grey wolf optimized convolutional neural networks (IGWO-CNN) was built. Finally, the intelligent analysis model of compaction quality was embedded into the simulation model of concrete rolling construction. Based on real-time monitoring data, the simulation model was driven and the simulation process was adjusted adaptively. Taking the construction of a roller compacted concrete dam in China as an example, the validity and superiority of this model are proved.
subject: Articles
  topic: section
subject: construction simulation
  topic: keywords
subject: concrete rolling construction
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: compaction quality
  topic: keywords
subject: improved generative adversarial networks
  topic: keywords
subject: improving grey wolf optimized convolutional neural networks
  topic: keywords
creator: Geng Zhang
creator: Binping Wu
creator: Bingyu Ren
creator: Tao Guan
creator: Jia Yu
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-11-04
publication date: 2025-11-04
starting page: 843
ending page: 859
doi: 10.3846/jcem.2025.24061

Advancing civil infrastructure with digital twins: a review of applications and challenges
type: item
description: The digital twins (DTs) technology has emerged as a ground-breaking approach in the management and maintenance of civil infrastructure, providing a virtual representation of physical systems which are continuously updated with realtime data from IoT sensors and simulations. Initially introduced in the manufacturing sector, the concept of digital twins has been extended to civil engineering, offering a significant potential for real-time monitoring, predictive maintenance, optimized asset management, and enhanced decision-making. This paper provides a comprehensive survey of the applications of the digital twins technology in civil infrastructure, with a particular focus on structural health monitoring (SHM), predictive maintenance, smart city frameworks, and disaster response systems. By reviewing existing methodologies, case studies, and practical implementations, this paper highlights the transformative impact of DTs in improving the efficiency, safety, and sustainability of infrastructure systems, including bridges, buildings, and transportation networks. Despite the numerous advantages of DTs, several challenges impede their widespread adoption in civil engineering. These challenges include high implementation costs due to the need for sophisticated sensors, high-performance computing, and advanced simulation tools. Additionally, data integration and interoperability issues between various data sources and platforms hinder seamless adoption. Cybersecurity risks associated with real-time monitoring systems and the protection of critical infrastructure are also discussed. This survey identifies these barriers and outlines the necessary technological advancements which may help overcoming the barriers. These include standardized data formats, enhanced AI-driven predictive models, and scalable cloud solutions, among others. This paper concludes by highlighting future research directions to address the identified challenges, emphasizing the need for collaboration acr…
subject: Articles
  topic: section
subject: digital twins
  topic: keywords
subject: civil infrastructure
  topic: keywords
subject: structural health monitoring
  topic: keywords
subject: AI-driven predictive model
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Hessam Kaveh
creator: Reda Alhajj
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-10-30
publication date: 2025-10-30
starting page: 828
ending page: 842
doi: 10.3846/jcem.2025.24921

Quotation impact factors for general contractors: a viewpoint from private sectors in Taiwan
type: item
description: The quotes provided by private sector entities to the project’s general contractor vary based on actual considerations. This research aims to identify the factors that influence these quotes for general contractors. Through an extensive review of literature and interviews with experts, four main aspects were identified, encompassing a total of 38 impact factors related to quoting patterns. A survey was conducted among construction professionals, resulting in 123 valid responses out of 146 distributed surveys. The survey’s validity was confirmed with a Cronbach’s Alpha value exceeding 0.7. Subsequently, a factor analysis was performed, confirming 38 impact factors and their respective weights. The results from the Principal Component Analysis (PCA) offer valuable managerial insights, highlighting the most impactful factors: external demand, corporate adjustment, and financial condition. These findings provide practical guidance for practitioners. These insights encompass areas such as information sharing, project limitations, and subcontractor capabilities. Contrary to stereotypes presented in prior research, factors such as reputation and social connections were found to not significantly impact quotation outcomes.
subject: Articles
  topic: section
subject: quotation factor
  topic: keywords
subject: construction management
  topic: keywords
subject: subcontractor
  topic: keywords
subject: survey
  topic: keywords
subject: PCA
  topic: keywords
creator: Jieh-Haur Chen
creator: Chih-Kuo Chang
creator: Hsi-Hsien Wei
creator: Ju-Ting Hou
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-10-16
publication date: 2025-10-16
starting page: 802
ending page: 810
doi: 10.3846/jcem.2025.24803

Evaluation model of construction quality data uncertainty: a study based on industry park project
type: item
description: This study proposes an evaluation and adjustment model for construction quality data uncertainty based on the research project of the Sichuan Provincial Building Industry Park. The core objective is to address the variability in concrete strength data caused by multiple factors during the construction process through a data-driven approach, which enables the accurate prediction of concrete strength. By analyzing construction quality data across different strength grades and curing ages, this study establishes a dynamic model centered on “data fitting” and “data prediction”. The model defines a reliability range for construction quality specific to the project, which facilitates the cleansing of outlier data and the adjustment of biased data. The results demonstrate that the model significantly enhances the reliability of the construction quality data. Further analysis of data from the same construction site revealed that it is highly applicable, and the date adjustment become more concentrated and stable. The fitting coefficient of this model approaches 1, which significantly improving the representativeness and reliability of the strength data. The findings provide a scientific basis for the dynamic assessment and optimization of construction quality, and robust support for quality control and prediction during the construction process.
subject: Articles
  topic: section
subject: construction quality management
  topic: keywords
subject: uncertainty evaluation
  topic: keywords
subject: strength prediction
  topic: keywords
subject: data adjustment model
  topic: keywords
subject: dynamic assessment
  topic: keywords
creator: Changhao Fu
creator: Weijie Xu
creator: Junchi Liu
creator: Qingzi Ge
creator: Hang Bai
creator: Tong Guo
creator: Tao Wang
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-10-15
publication date: 2025-10-15
starting page: 783
ending page: 801
doi: 10.3846/jcem.2025.24607

Barriers and facilitators of promoting work-related injury insurance in the construction industry: lessons from China
type: item
description: Work-related injury insurance (WRII) is essential for protecting workers’ rights and enhancing workplace safety, yet its promotion in construction faces challenges. This study investigates WRII adoption barriers and facilitators in China’s construction sector using a mixed-methods approach. Semi-structured interviews with 14 industry experts were conducted, followed by thematic analysis to identify key barriers. An analytic hierarchy process was then applied to prioritize these barriers. The findings reveal six main barriers: (1) cumbersome claim process, (2) incomplete law and regulation system, (3) weak right-protection awareness of construction workers, (4) weak legal consciousness of contractors, (5) unreasonable compensation treatment and fund management, and (6) difficulties in employment relationship identification. To address these challenges, the study proposes five targeted measures: (1) simplifying the claim process, (2) promulgating authoritative and detailed laws and regulations, (3) strengthening supervision and law enforcement, (4) strengthening advocacy, and (5) improving insurance compensation and fund management. These findings offer practical guidance for policymakers to enhance WRII effectiveness in China’s construction industry and provide insights applicable to other developing countries. Despite limitations related to expert judgment and sector focus, the study offers valuable recommendations for future research on cross-industry comparisons and digital solutions for WRII.
subject: Articles
  topic: section
subject: construction workers
  topic: keywords
subject: work-related injury insurance
  topic: keywords
subject: China
  topic: keywords
subject: barrier and facilitators
  topic: keywords
subject: qualitative research
  topic: keywords
subject: AHP
  topic: keywords
creator: Baoquan Cheng
creator: Ruoyu Jin
creator: Xiaowei Luo
creator: Jianling Huang
creator: Huihua Chen
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 763
ending page: 782
doi: 10.3846/jcem.2025.24602

Performance comparison of various time-series forecasting models for bridge sufficiency rating prediction
type: item
description: The rapid increase in the number of bridges worldwide has intensified the need for effective maintenance strategies to ensure structural safety and economic efficiency. Accurate predictions of future bridge performance are essential for preventing unexpected failures and optimizing road network maintenance planning. However, existing prediction models frequently overlook the time-series characteristics inherent in bridge inspection data, thereby limiting their accuracy. This study aims to develop improved prediction models by integrating sequential data patterns using advanced deep-learning techniques. Data from the National Bridge Inventory were utilized. As most NBI data lacked explicit sequential structures, preprocessing techniques were applied to generate meaningful time-series patterns. Deep-learning models, including deep neural networks (DNNs), convolutional neural networks, long short-term memory (LSTM), and Transformers, were developed and evaluated using cross-validation to optimize their performance. Results showed that the LSTM model improved prediction accuracy by approximately 46% compared to the baseline DNN model. The Transformer model further improved accuracy by approximately 7% over the LSTM, highlighting its superior ability to capture long-term dependencies. These findings highlight the potential of the Transformer model as a powerful tool for predicting bridge performance, thereby supporting effective maintenance planning and reducing the risk of structural failures.
subject: Articles
  topic: section
subject: bridge performance
  topic: keywords
subject: deep learning
  topic: keywords
subject: maintenance
  topic: keywords
subject: road networks
  topic: keywords
subject: time series
  topic: keywords
subject: transformer
  topic: keywords
creator: Yangrok Choi
creator: Youngjin Choi
creator: Kyungrok Kwon
creator: Jin Hyuk Lee
creator: Jung Sik Kong
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-10-16
publication date: 2025-10-16
starting page: 811
ending page: 827
doi: 10.3846/jcem.2025.24914

BIM-based digital twin for the management of a railway station
type: item
description: Building Information Modelling (BIM) has positioned as one of the main project methodologies into the AECO (Architecture, Engineering, Construction and Operations) sector. However, its implementation entails important barriers such as hardware and software requirements or BIM skills and education. The main aim of this research was to overcome those implementation barriers providing full accessibility to BIM benefits, especially in the infrastructure management phase leading to a digital twin (DT) of the built environment. Furthermore, the paper shows the technical aspects of the implementation of BIM information as well as the external functionalities, i.e., real time information or data queries, which are commonly define in BIM-IoT topic rather than BIM-FM interoperability. The research is based on the project for the northern covering and new building of the Bidebieta-Basauri passenger station, located on the Bilbao-Orduña commuter line in the Autonomous Community of the Basque Country, Spain. The final result is a BIM-based online web platform with Full access to the BIM model of the infrastructure, linked with other functionalities based on technologies such as Internet of Things (IoT), Bigdata or Cloud Computing.
subject: Articles
  topic: section
subject: BIM
  topic: keywords
subject: computer programming
  topic: keywords
subject: IoT
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: intelligent management system
  topic: keywords
subject: railway stations
  topic: keywords
creator: Rubén Muñoz Pavón
creator: Marcos García Alberti
creator: Jorge Jerez Cepa
creator: Tomás Luis Ripa Alonso
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 747
ending page: 762
doi: 10.3846/jcem.2025.24091

Evaluation of the effect of fineness of fly ash in type I and type II Portland cement blended mortars
type: item
description: This study evaluates the effects of fineness-modified Class F fly ash (FA) on the performance of Type I and Type II Ordinary Portland Cement (OPC) mortars. FA was mechanically ground to achieve 90% (GFA1) and 98% (GFA2) passing through a 45-µm sieve, replacing OPC at 10%, 30%, and 50% by weight. Comprehensive testing included compressive strength, water demand, setting time, heat of hydration, acid resistance, and microstructural analysis. Results demonstrated that increasing FA fineness reduced water demand by up to 6.3% (Type I) and 5.9% (Type II) at 30% replacement, while enhancing long-term strength. Mortars with 10% GFA2 achieved 58.6 MPa at 56 days. Type II OPC blends exhibited superior acid resistance, with mass loss reductions of 43% (50% GFA2) compared to Type I, attributed to lower C₃A content and denser microstructures. Hydration heat decreased by 10.9% in Type II OPC, further reduced by 40% with 50% FA replacement. The microstructural analysis confirmed reduced ettringite formation and enhanced compactness in FA-modified mortars, correlating with improved durability. These findings highlight the viability of finely ground FA as a sustainable supplementary material, enabling highvolume FA utilization (up to 50%). The study provides critical insights for optimizing greener cement formulations, particularly in regions prioritizing Type II OPC for sulfate resistance and moderate heat applications, advancing Malaysia’s net-zero carbon goals through reduced clinker reliance.
subject: Articles
  topic: section
subject: fineness of fly ash
  topic: keywords
subject: type I & II ordinary Portland cement
  topic: keywords
subject: setting time
  topic: keywords
subject: soundness
  topic: keywords
subject: heat of hydration
  topic: keywords
creator: Hari Krishna Subramaniam
creator: U. Johnson Alengaram
creator: Wan Zurina Wan Jaafar
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-09-10
publication date: 2025-09-10
starting page: 729
ending page: 746
doi: 10.3846/jcem.2025.24419

Development of an automated surface crack detection and BIM-integrated management system for concrete bridges
type: item
description: Bridge inspection work typically requires inspectors to capture hundreds to thousands of images, consuming substantial time for review. This research developed an “Automated Crack Image Cloud Detection System” and the “Auto Predictor” application, enabling automatic crack identification and deterioration image recognition through uploads. This platform integrates with the “Bridge BIM Cloud Management System”, connecting crack information with three-dimensional models. Engineers can create BIM models based on structural design drawings, while inspectors can photograph cracks and integrate relevant information. The study utilized deterioration images from long-term bridge inspections in Taiwan, covering various real-world environmental conditions. Through effective deterioration labeling strategies and comparing YOLOv4 and YOLOv7 algorithms with recommended parameters, an optimal model was obtained for system implementation. Research results demonstrate that the “Automated Crack Image Cloud Detection System” successfully identified cracks in bridge inspections and short beam shear test specimens. The deep integration with the “Bridge BIM Cloud Management System” enables automatic component crack identification and generates location charts, providing decision-makers with intuitive visual data. The YOLOv7-based model achieved a mean Average Precision (mAP) of 87.64%, significantly improving bridge inspection efficiency and demonstrating exceptional application potential.
subject: Articles
  topic: section
subject: bridge inspection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: crack detection
  topic: keywords
subject: automation
  topic: keywords
subject: BIM
  topic: keywords
subject: management
  topic: keywords
creator: Hsiao-Yung Hsieh
creator: Kuang-Yen Liu
creator: SzuMin Kang
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-09-04
publication date: 2025-09-04
starting page: 710
ending page: 728
doi: 10.3846/jcem.2025.24094

Prediction of sewage pipeline construction duration by introducing machine learning and deep learning approaches
type: item
description: Establishing project costs in construction is crucial for project success, typically done through regression methods for prediction. While these methods are common, novel regression methods are less practiced in construction management. This study explores both traditional and modern regression techniques, analyzing data from 83 sewage pipeline projects in South Korea. The study implemented state-of-the-art frameworks, including hyperparameter optimization and k-fold cross-validation, to evaluate statistic, machine learning and deep learning based regression models using R2 score, RMSE, MAE, and MSE. Results revealed that performance metrics don’t always align with predictive accuracy. For instance, the random forest regressor achieved the best R2 score of 0.847 but ranked fifth in prediction accuracy. Moreover, polynomial regression outperformed novel methods with a 98.790% accuracy across the validation dataset.
subject: Articles
  topic: section
subject: construction management
  topic: keywords
subject: sewage pipeline construction
  topic: keywords
subject: statistical regression
  topic: keywords
subject: machine learning regression
  topic: keywords
subject: deep learning regression
  topic: keywords
creator: Sang-Jun Park
creator: Norhane Nour
creator: Kang Young Lee
creator: Ju-Hyung Kim
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 687
ending page: 709
doi: 10.3846/jcem.2025.23472

Exploring the use of in-house sodium silicate from agro-industrial by-products in pervious geopolymer concrete
type: item
description: The extraction of in-house sodium silicate (IHS) as an alternative to commercial silicate in geopolymer pervious concrete (GPC) is the focus of this research. The IHS was developed from rice husk ash (RHA) and treated palm oil fuel ash (TPOFA) using the hydrothermal method. Class F Fly Ash (FA) and Ground Granulated Blast Furnace Slag (GGBS) were used as precursors in a 70:30 ratio. Steel slag aggregate (SSA) was used to wholly replace the conventional aggregates. Palm kernel shell biochar (PKS-BC) at various weight percentages between 1 and 5% was used to replace coarse aggregates (CA). GPC specimens were prepared using 10 M sodium hydroxide (NaOH) and one of the SS: commercial SS, RHA-based IHS, and a ‘Hybrid SS’ (commercial SS: TPOFA-based IHS – 50:50). The findings revealed that due to the toughness, surface roughness, and shape of the SSA, the compressive strength of SSA-based GPC specimens produced higher strength compared to crushed granite aggregate (CGA)-based GPC. ‘Hybrid SS’ and RHA-based IHS yielded slightly higher compressive strengths in GPC specimens compared to commercial SS-based GPC specimens. This finding proved that the appropriate ratio of silica source with NaOH facilitates the development of SS in the development of GPC.
subject: Articles
  topic: section
subject: geopolymer pervious concrete
  topic: keywords
subject: in-house sodium silicate
  topic: keywords
subject: hydrothermal method
  topic: keywords
subject: palm kernel shell biochar
  topic: keywords
subject: UV spectrophotometer
  topic: keywords
creator: Robberta Renuka Lazarus
creator: U. Johnson Alengaram
creator: Wan Zurina Wan Jaafar
creator: Sai Hin Lai
creator: Muhammad Shazril Idris Bin Ibrahim
creator: Karthick Srinivas M
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-13
publication date: 2025-08-13
starting page: 670
ending page: 686
doi: 10.3846/jcem.2025.23785

Integration of BIM and ar with VSLAM to assist in construction site inspection
type: item
description: Building Information Modeling (BIM) has been widely adopted for construction inspections due to its ability to integrate multiple data sources. Engineers use BIM to identify and review site issues, yet inspection systems face several challenges. Firstly, positioning inspection areas on a construction site using BIM with Augmented Reality (AR) requires complex model manipulation. Additionally, signal or Internet connectivity issues may limit positioning technologies. Secondly, human error or interference is common in traditional inspection processes due to their complexity. To overcome these barriers, this research applied BIM and AR with Visual Simultaneous Localization and Mapping (VSLAM) to help inspectors quickly and effectively record construction defects as photographs with notes and their locations. An efficient approach is proposed to integrate BIM and AR with VSLAM, and a prototype is developed to validate and demonstrate how the proposed system can assist a site inspector in performing quality management, even offline. The system uses a two-phase indoor positioning method: initial localization via visual markers and real-time tracking with VSLAM, enabling precise defect tracking and efficient model adjustments. While significantly improving inspection accuracy and efficiency, its performance is affected by environmental factors like lighting and marker placement, providing insights for future refinement.
subject: Articles
  topic: section
subject: Building Information Modeling (BIM)
  topic: keywords
subject: Visual Simultaneous Localization and Mapping (VSLAM)
  topic: keywords
subject: Augmented Reality (AR)
  topic: keywords
subject: construction site inspection
  topic: keywords
creator: Wei-Liang Kuo
creator: Bo-Kai Huang
creator: Shang-Hsien Hsieh
creator: Yuan-Hao Tsai
creator: Yun-Tsui Chang
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-08
publication date: 2025-08-08
starting page: 646
ending page: 669
doi: 10.3846/jcem.2025.24360

Sustainable design of recycled concrete using shape optimization and carbon dioxide emission based on LCA
type: item
description: Switching from waste concrete disposal to recycling is urgently needed to enhance resource efficiency and reduce carbon emissions. This paper proposes a sustainable design framework for recycled concrete, incorporating shape optimization and carbon dioxide (CO2) emission analysis using life cycle assessment (LCA). Using recycled concrete in infrastructure projects, this paper develops a carbon dioxide emissions accounting model based on LCA. Two water-cement ratios (WCR) and four recycled concrete aggregate replacement rates (RCARR) were tested on two natural aggregate concrete (NAC) and six recycled aggregate concrete (RAC) samples. Furthermore, four shapes options for the RAC structural member were designed, optimized, and compared. The G35 Expressway slope projects were used as a case study. The results showed that the regular hexagonal RAC structural member was selected for the project, achieving a carbon reduction rate of about 9%. The study also found that 1) life cycle carbon emission decreases with the increase of WCR and RCARR, respectively; 2) compared to NAC, the key processes of carbon emission reduction of RAC include the raw material acquisition and transportation stage as well as the carbonization absorption stage; 3) there is a transport distance threshold, beyond which the life cycle CO2 emissions of RAC exceed those of NAC.
subject: Articles
  topic: section
subject: recycled concrete
  topic: keywords
subject: carbon dioxide emissions
  topic: keywords
subject: shape design optimization
  topic: keywords
subject: infrastructure
  topic: keywords
creator: Weina Zhu
creator: Jianing Li
creator: Linghan Wang
creator: Xiaodong Li
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-08
publication date: 2025-08-08
starting page: 631
ending page: 645
doi: 10.3846/jcem.2025.24108

Multi-Criteria Decision Methods in the evaluation of social housing projects
type: item
description: The evaluation of social housing projects is a complex process that requires the consideration of multiple factors and dimensions to make informed decisions to address the emerging challenges of contemporary urbanization and achieve more resilient and sustainable communities. This study's comprehensive review of the existing literature on the use of Multi-Criteria Decision Methods (MCDM) in evaluating social housing projects was undertaken. An exhaustive analysis of a bibliographic set of 93 articles published between 1994 and march 2025 was conducted. It was noted that the articles analyzed different phases of the construction process, from planning and design to implementation and maintenance. Significant trends in the use of MCDM were identified, highlighting the prevalence of crisp number-based approaches and the emergence of modern techniques such as fuzzy logic and neutrosophic logic. Among the most widely used methods were AHP and TOPSIS, both pioneering methods. In addition, there was an increasing focus on sustainability in project evaluation, encompassing environmental, social, economic, and technical aspects. Consequently, this literature review serves as a guide for incorporating multi-criteria evaluation strategies to improve constructability, especially in social housing projects, taking sustainability into consideration.
subject: Articles
  topic: section
subject: multi-criteria decision-making
  topic: keywords
subject: decision making
  topic: keywords
subject: MCDM
  topic: keywords
subject: social housing project
  topic: keywords
subject: social housing
  topic: keywords
subject: constructability
  topic: keywords
subject: sustainability
  topic: keywords
creator: Ximena Luque Castillo
creator: Victor Yepes
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 608
ending page: 630
doi: 10.3846/jcem.2025.24425

Modelling progressive failure of segmental linings in shield tunnels
type: item
description: Many engineering accidents in shield tunnels occur because localized failure induces the progressive damage or failure of segmental linings. At present, knowledge of the trigger mechanism for this catastrophe is still very limited. While it is difficult to conduct full-scale tunnel failure tests in actual strata, it is feasible to perform numerical simulations of tunnel failure. However, before simulating tunnel failure, it is important to establish a sophisticated model that considers the damage behaviour of segmental linings. In this paper, based on the concrete damage plasticity model and element deletion technology, a sophisticated model of segmental linings was established. The model was verified by data from current structural loading tests. To improve the calculation efficiency, the model was properly simplified. Then, it was applied to simulate the progressive failure of segmental linings due to localized failure in a shield tunnel considering complex soiltunnel interactions. The numerical analysis showed that concrete was damaged in the segments adjacent to the localized failure zone. Some of the stirrups and erection bars in these segments yielded, while the upper and lower rows of steel bars as well as the bolts connecting the segments had sufficient safety redundancy.
subject: Articles
  topic: section
subject: shield tunnel
  topic: keywords
subject: CDP model
  topic: keywords
subject: element deletion
  topic: keywords
subject: damage of segmental lining
  topic: keywords
subject: progressive failure
  topic: keywords
subject: soil-tunnel interaction
  topic: keywords
creator: Tianqi Zhang
creator: Bangguo He
creator: Zhitong Chen
creator: Xun Li
creator: Gang Zheng
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 596
ending page: 607
doi: 10.3846/jcem.2025.24326

How emotional intelligence affects inter-organizational relationships in megaprojects: the boundary spanning perspective
type: item
description: Boundary Spanners (BSs) substantially impact relationship development in megaprojects. Yet, previous research only focused on the emotional intelligence (EI) of single-level BSs, e.g., megaproject managers and decision-makers. By clustering BSs into higher-level BSs (HBSs) and middle-lower-level BSs (M&LBSs), this article aims to investigate how to enhance inter-organizational relationships through different-level BSs’ EI and boundary-spanning behaviors including ambassadors, coordination and information scanning behaviors. Data from 119 HBSs and 171 M&LBSs in 15 megaprojects were first collected with a questionnaire survey. 18 semi-structured interviews were then undertaken to give a deeper explanation to the underlying mechanism. The results show that HBSs’ EI is directly and positively associated with inter-organizational relationships. However, the benefit of M&LBSs’ EI in megaprojects can only be realized through the mediating effect of their boundary-spanning behaviors. HBSs’ EI is effective in improving external ambassadors’ activities. In contrast, M&LBSs’ EI is effective in improving information scanning. This study advanced EI and relationship study in megaprojects, stating the mechanism between these two variables varies for different-level boundary spanners.
subject: Articles
  topic: section
subject: boundary spanner
  topic: keywords
subject: boundary spanning behavior
  topic: keywords
subject: emotional intelligence
  topic: keywords
subject: inter-organizational relationships
  topic: keywords
subject: mixed-method
  topic: keywords
subject: megaproject
  topic: keywords
creator: Tingting Cao
creator: Giorgio Locatelli
creator: Nigel Smith
creator: Lianying Zhang
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 580
ending page: 595
doi: 10.3846/jcem.2025.24102

Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
type: item
description: In this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. The analysis suggests that optimal predictive performance is achieved when the number of control variables is approximately four. Additionally, further research shows that increasing the dataset size significantly enhances the model’s predictive capability. This study highlights the scientific rigor and precision of decisionmaking in preventing construction accidents and provides empirical evidence for construction safety management. The research in this paper not only enriches the connotation of the grey system prediction model theoretically, but also provides a data-driven decision support tool for urban construction and safety accident prevention in practice.
subject: Articles
  topic: section
subject: multivariable grey model
  topic: keywords
subject: construction safety management
  topic: keywords
subject: variable selection optimization
  topic: keywords
subject: prediction accuracy
  topic: keywords
subject: data size effect
  topic: keywords
creator: Jian Liu
creator: Ye He
creator: Rui Feng
creator: Qinlin Chu
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 561
ending page: 579
doi: 10.3846/jcem.2025.24348

Is contractual flexibility always good for relationship quality among the parties in construction projects?
type: item
description: Contractual flexibility as an effective way to maintain and increase parties’ relationship quality has been widely introduced to contracting in construction projects. However, conflicting evidences exist regarding their effectiveness on relationship quality based on transaction cost theory. Building on two distinct bases for contractual flexibility: content flexibility, rely on flexible content incorporated in initial contract content, versus executing flexibility, rely on parties’ relationship instead of contract contents, this study examines how contractual flexibility effect relationship quality under different transaction attributes (asset specificity, environmental uncertainty, and behavioral uncertainty). Results from a sample of 312 parties reveal that content flexibility and executing flexibility positively influence relationship quality, with executing flexibility having a stronger association than content flexibility. Yet, important conditions exist. The moderating analysis demonstrates that when asset specificity or environmental uncertainty is high, executing flexibility, not content flexibility, matters more to relationship quality. In contrast, when behavioral uncertainty is high, content flexibility, not executing flexibility, matters more to relationship quality. These findings suggest that contractual managers should incorporate executing flexibility into contracts more sufficiently, and the appropriate contractual flexibility should match with transaction attributes.
subject: Articles
  topic: section
subject: contractual flexibility
  topic: keywords
subject: content flexibility
  topic: keywords
subject: executing flexibility
  topic: keywords
subject: relationship quality
  topic: keywords
subject: transaction attributes
  topic: keywords
subject: asset specificity
  topic: keywords
subject: environmental uncertainty
  topic: keywords
subject: behavioral uncertainty
  topic: keywords
creator: Qianxing Ding
creator: Lianying Zhang
creator: Tak Wing Yiu
creator: Ziqing Liu
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-07-15
publication date: 2025-07-15
starting page: 544
ending page: 560
doi: 10.3846/jcem.2025.24320

Mapping publications on value creation in construction project settings: a mixed bibliographic and bibliometric analysis
type: item
description: The worldwide interest on value creation has triggered an increasing number of articles, especially after a new paradigm named service-dominant logic was announced in 2004. However, limited research exists on the whole picture of value creation in the field of construction projects. Therefore, the current study is expected to reveal the status and future directions of value creation studies under construction projects. A number of 63 journal articles between 2004 and 2022 were analysed via a combined bibliographic and bibliometric approach, which covers annual publication, institutional and regional contribution, author contribution and keyword analyses. Results indicated that most published articles were based on the developed economies, such as the United Kingdom, Finland and Norway. Keywords such as megaproject, governance, social value and co-creation are emphasised, as analysed using CiteSpace software. Three implications, namely, value creation in developing areas, megaproject value creation, and perceived value perspective, are highlighted. The better understanding of the relevant literature could largely benefit academic peer researchers on value creation. Moreover, the holistic review of the literature body efficiently identifies the knowledge gaps and outlines avenues for following scholars and facilitating high-quality development of the construction engineering industry.
subject: Articles
  topic: section
subject: project value
  topic: keywords
subject: construction engineering and management
  topic: keywords
subject: service-dominant logic
  topic: keywords
subject: CiteSpace software
  topic: keywords
subject: literature review
  topic: keywords
creator: Ting Wang
creator: Zidan Tian
creator: Yan Liu
creator: Tianyan Wu
creator: Qinghua He
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 532
ending page: 543
doi: 10.3846/jcem.2025.23941

Sub-activity analysis by using wristband-type wearable health devices to measure construction workers’ physical demands
type: item
description: Understanding and managing the physical demands of construction workers is critical to their safety, health, and productivity. Construction tasks, especially cyclic and non-cyclic activities are highly variable. Cyclic tasks involve repetitive actions, which if not well-distributed, can lead to cumulative fatigue and long-term health issues. Conversely, non-cyclic tasks are unpredictable and irregular, making it challenging to allocate workloads effectively. Despite these challenges, few studies have investigated continuous physical demands for both task types, particularly in dynamic, onsite construction environments. Filling this gap is essential for developing practical strategies to improve workload allocation, mitigate health risks, and optimize workforce management. This study addresses this critical gap by using lightweight wearable wristbands equipped with heart rate (HR) biosensors to monitor workers’ activities without disrupting their tasks. The percentage of heart rate reserve (%HRR) is calculated to quantify the continuous physical demands of 10 construction workers performing both cyclic and non-cyclic tasks across two construction sites over three weeks. Results revealed significant workload variations between task types and work patterns. For instance, stationary tasks (‘work without moving’) strongly influenced %HRR for rebar workers, while dynamic tasks (‘work with moving’) had a greater impact on form workers. Additionally, while some daily average %HRR values fell below the 33% threshold, extended high-intensity periods (exceeding 40% HRR for over 30 minutes) posed potential health and safety risks for construction workers. This research demonstrates the potential of long-term HR monitoring to address workload disparities, ensure balanced task allocation, and reduce health risks for construction workers.
subject: Articles
  topic: section
subject: physical demand
  topic: keywords
subject: % heart rate reserve
  topic: keywords
subject: cyclic and non-cyclic work
  topic: keywords
subject: work load
  topic: keywords
subject: construction worker
  topic: keywords
creator: Jiayao Wang
creator: JoonOh Seo
creator: Yue Gong
creator: Ming-Fung Fransic Siu
creator: Sungjoo Hwang
creator: Masood Khan
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 516
ending page: 531
doi: 10.3846/jcem.2025.24058

Methodology for assessing and controlling the risk grade of structural columns threatened by blast incident
type: item
description: Columns are important structural components and are threatened by local conflicts and explosion accidents. This paper presents a fuzzy-based risk assessment framework to evaluate the potential blast disasters associated with structural columns. The framework establishes an indicator system and incorporates risk functions and a fuzzy transformation system for blast risk assessment. The priority weights of critical attributes are determined using a fuzzy analytic hierarchy process (FAHP) approach, and the risk factor (RF) is calculated via the aggregation of foundational fuzzy evaluations. The feasibility and applicability of the framework are demonstrated through the risk level assessment of five example columns. The framework’s rationality is further validated by comparing the onrisk grades of identical cases, as assessed by the proposed framework and alternative methods. The study results indicated that the framework can effectively discern the risk range of desired grade rankings and ascertain the risk grade. By integrating the obtained attribute ranking and hierarchical structure, the framework facilitates the identification of potent strategies for controlling blast risk. The resulting risk-grade findings serve as a foundation for the identification of priority protection and anti-explosion design of structural columns.
subject: Articles
  topic: section
subject: blast disaster
  topic: keywords
subject: multi-criteria risk assessment
  topic: keywords
subject: fuzzy theory
  topic: keywords
subject: structural column
  topic: keywords
creator: Peng Sun
creator: Xiaomeng Hou
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 502
ending page: 515
doi: 10.3846/jcem.2025.24093

Integrating LINE BOT and Building Information Model to develop construction information management system
type: item
description: In the lifecycle of construction projects, the participation of various specialized members often leads to challenges in recording or retrieving information in real time. This can result in missing data or inaccurate project control decisions, due to the inability to access essential information swiftly and accurately. Recognizing the ubiquitous use of instant messaging platforms on mobile phones and the widespread adoption of Building Information Modeling (BIM) for information storage and management, this research proposes an innovative integration of chatbots with BIM to establish a robust construction information management system. Utilizing “LINE”, a popular communication software, as the foundation, this study develops four specialized chatbots (LINE BOTs) tailored for different phases of construction projects, namely design, construction, and maintenance. These chatbots employ a rule-based and conversational approach to aid construction personnel in real-time recording and retrieval of project information. During the maintenance phase, users can also access relevant equipment objects through the BIM model using mobile smart devices, further improving maintenance efficiency. Moreover, the chatbots are equipped with a string-matching mechanism to enhance the precision of data recording and retrieval processes. The effectiveness of this system is demonstrated through a case study on a public construction project.
subject: Articles
  topic: section
subject: chatbot
  topic: keywords
subject: LINE BOT
  topic: keywords
subject: building information modeling (BIM)
  topic: keywords
subject: construction projects
  topic: keywords
subject: mobile information management
  topic: keywords
creator: Yu-Jen Cheng
creator: Minh-Tu Cao
creator: Shih-Hsu Wang
creator: Ngoc-Mai Nguyen
creator: Shih-Sin Chen
creator: Wei-Chih Wang
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 482
ending page: 501
doi: 10.3846/jcem.2025.23764

Measurement and spatiotemporal evolution characteristics analysis for the provincial development level of intelligent construction in China
type: item
description: Intelligent Construction (IC) is emerging as a transformative approach within the architecture, engineering, and construction (AEC) industry, garnering significant global attention. There exist considerable disparities in the development levels of IC across various provinces in China, leading to uneven advancement that complicates precise policy formulation and differential implementation. Previous studies have primarily evaluated IC at the project and enterprise levels, thus lacking a comprehensive measure of the provincial IC development level. To bridge this gap, this study introduces a quantitative method to assess provincial IC development levels in empirical data, analyzing their driving factors and spatiotemporal evolution. Initially, based on the Politics-Economy-Society-Technology (PEST) analysis model, 16 measurement indexes were identified through a combination of literature review and expert interviews. Original data for these indexes were acquired via policy and media news mining, along with literature and patent indexing, etc. Subsequently, a quantification method for each index was established. The “analytic network process (ANP), entropy weight, and game theory” integration method was used to calculate combination weights. Finally, the development level of IC was quantitatively measured based on the cloud matter-element model, and the spatiotemporal evolution characteristics of the provincial development level in China from 2012 to 2022 were analyzed. The results indicate that (1) the development level of IC in China is divided into four levels, and the overall development level is relatively low, with only Beijing, Shanghai, and Shandong ranking at level I. (2) The development level shows a trend of increasing from northwest to southeast, with policy and technological factors being the main driving forces. (3) There is a significant spatial positive correlation between the development levels of provinces, and their spatial agglomeration effects are …
subject: Articles
  topic: section
subject: intelligent construction
  topic: keywords
subject: development level
  topic: keywords
subject: spatiotemporal evolution
  topic: keywords
subject: measurement system
  topic: keywords
subject: China
  topic: keywords
creator: Yudan Dou
creator: Xueya Yan
creator: Xin Guo
creator: Shengbin Ma
creator: Longzhu Zhong
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 418
ending page: 437
doi: 10.3846/jcem.2025.23767

A relative measurement and monitoring of OTTV of glass facade passive building in tropical climate
type: item
description: Efficient and productive buildings are vital to sustainable cities, significantly contributing to Sustainable Development Goals (SDGs) 3, 7, 11, and 12. Urban areas are responsible for 80% of global energy consumption, with buildings accounting for 40%. Achieving a healthy and comfortable indoor thermal envelope depends on various factors including building function, location, layout design, openings, and materials. The building facade, particularly glass facades, is a significant contributor to both energy performance and occupant comfort. Despite their importance, few studies focus on real-time measurement and monitoring of the Overall Thermal Transfer Value (OTTV) of passive buildings, especially with glass facades. Glass allows natural light and heat exchange, impacting the overall energy performance and quality of indoor environments. This study investigates the real-time impact of temperature variations on the OTTV of glass facade passive buildings from 8 am to 5 pm, focusing on Malaysia’s tropical climate. The study’s findings revealed that the OTTV varies from 42.642 W/m2 at 11:30 am to 80.341 W/m2 at 10:30 am. The study contributes to the body of knowledge by providing valuable insights regarding the dynamic thermal behaviour of the passive building envelope. Specifically, it demonstrates how OTTV varies with changing climatic conditions such as temperature fluctuations and solar radiation.
subject: Articles
  topic: section
subject: sustainable development goals
  topic: keywords
subject: overall thermal transfer value
  topic: keywords
subject: building envelope
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: facade materials
  topic: keywords
creator: Anees Ahmed Vighio
creator: Rozana Zakaria
creator: Katarzyna Glinska-Lewczuk
creator: Vikneswaran Munikanan
creator: Noraziah Wahi
creator: Eeydzah Aminuddin
creator: Faridahanim Ahmad
creator: Kamaliah Mohd Saha
creator: Thong Jin Wen
creator: Joanna A. Pawlowicz
rights: Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
date: 2025-05-22
publication date: 2025-05-22
starting page: 450
ending page: 462
doi: 10.3846/jcem.2025.23717