Journal of Civil Engineering and Management
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 ...
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.
Determining safety-related critical success factors in petrochemical projects through an extensive fuzzy risk assessment approach
The construction phase of petrochemical units encounters many risks, a large number of which leads to death and disabling injuries. The purpose of this study is to provide a framework to determine safety-related critical success factors (CSFs) in petrochemical construction projects through risk assessment. The proposed approach involved two phases. At first, 15 potential risks were identified through a review of previous studies and interviews with experts. Then a combination of failure mode and effect analysis (FMEA), criteria importance through inter-criteria correlation (CRITIC) and technique for order preference by similarity to the ideal solution (TOPSIS) in a fuzzy environment was used for risk assessment, and the risks were classified based on their score. In Phase 2, safety-related CSFs were identified and ranked using the solutions provided for each risk and by consulting with experts. Results showed that falling from height and structural collapse due to excavation operations were the most and least important risks, respectively. Top management support was identified as the most important factor amongst the safety-related CSFs. The findings of this paper assist petrochemical construction project managers to adopt a structured approach for the risk assessment and determination of safety-related CSFs in their projects.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
An integrated vision-based dynamic collision risk assessment framework of workers and mobile machinery on construction site
The concepts of environmental impact index and spatial conflict degree have gained prominence in enhancing the controllability of collision accidents and mitigating the likelihood of collisions during construction processes. Nevertheless, prior studies predominantly focused on exploring collisions in terms of proximity and congestion between pairs of entities, thereby overlooking a comprehensive consideration of workers, construction machinery, environmental factors, and the spatial interaction of specific activities.To this end, this study aims to propose an integrated vision-based dynamic collision risk assessment framework by setting workers and mobile machinery as targeted research objectives and embedding a comprehensive risk assessment model in the proposed framework, thereby comprehensively assessing four types of risk factors (i.e., proximity, congestion, environmental impact index, and spatial conflict), and visualize the hierarchy of risk warnings. Firstly, a comprehensive risk assessment model was developed by using the fuzzy comprehensive evaluation method. This is followed by developing a dynamic risk assessment framework to extract the spatial information of the monitored objects by using computer vision as the underlying data of the risk factors. Finally, the proposed integrated framework was validated by an experimental study. This experiment’s safety risk assessment results are consistent with expectations, which largely illustrates the effectiveness of the evaluation model constructed in this paper. For the vision module, the accuracy of classification and monitoring is more than 95%, and the speed of the object detection algorithm to process the video is about 10 frames per second, which shows the feasibility of this study.
Integration of Lean Six Sigma into earned value management using system dynamics
A Few Lean Six Sigma (LSS) papers were studied during the construction project’s progress. This study proposes the integration of LSS, system dynamics (SD), and earned value management (EVM) as a comprehensive toolkit for datadriven decision-making. Significant waste- and quality-related interdependencies were identified using DEMATEL techniques with 27 Saudi construction experts. SD with two interdependent models of waste causes (WCs) and quality causes (QCs) were utilized to assess the project’s performance. Two metrics were employed: the sigma rating and the value-added ratio in the EVM generated by the developed SD model. The findings indicated that eliminating WCs had little impact on enhancing the project performance in the early stages of the project. The impact increased with the progress of the project. The improvement of the project quality was minimal for “increases of errors and omissions in design documents” and maximum for “increasing morale and attitude affect the quality of the project”.
Developing a data-driven failure decision-making framework for sustainable urban water management projects
Several degradation factors significantly impact the durability of water pipes in urban areas. However, limited research has comprehensively prioritized these factors to support data-driven maintenance and renewal decisions. Addressing this gap, this study identifies and ranks the failure factors affecting water pipeline infrastructure. A survey of 125 Egyptian water industry experts was conducted, and the collected data were analyzed using partial least squares-structural equation modeling as the decision-making framework. By incorporating insights from previous research and expert opinions, the research developed a robust failure decision-making model that provided significant insights into the primary factors contributing to water pipeline failures. Model analysis revealed that the “operational factor”, with an impact value of 0.543, was the most critical group of factors affecting pipeline failure. Following closely with an impact value of 0.480, was the “static factor”. Natural disasters (0.373), climate and weather conditions (0.325), and soil conditions (0.300) also contributed considerably. Following closely were “dynamic loads” (0.276), “aging and environmental factors” (0.250), and “third-party factors” (0.200), which had the least impact on the failure of the pipeline. This study has developed a novel failure decision-making model by synthesizing insights from previous studies, expert opinions, and empirical data on water pipeline failure.
Factors affecting knowledge sharing in blockchain technology in the Chinese construction industry
With the emergence of blockchain technology, knowledge sharing has become increasingly important for gaining a competitive advantage in any industry. However, it has not been fully implemented in the construction industry. This paper examines the factors influencing knowledge sharing in blockchain technology, using the Chinese construction industry as a case study. First, the study identified the key factors affecting knowledge sharing through a literature review and construction market research, classifying them based on the technology–organization–environment (TOE) framework. Next, the study employed focus group discussions to explore the relationships between pairs of factors and applied the interpretive structural modeling-matrix of cross-impact multiplication applied to classification (ISM-MICMAC) method to evaluate and prioritize these key factors. Finally, based on the empirical analysis, the paper discusses measures to promote knowledge sharing in the construction industry. This study contributes to the academic understanding of the factors influencing knowledge sharing and offers a fresh perspective for future research. Additionally, it provides practical insights to help the construction industry gain a competitive advantage and achieve sustainable development.
IOT-enabled framework for monitoring carbon emissions in the materialization phase of modular integrated construction
The potential of Modular Integrated Construction (MiC) to reduce carbon emissions (CEs) has led to increased attention on developing rigorous monitoring systems. Existing methods predominantly capture CEs of isolated MiC stages, overlooking nuances of arrangeable and reusable activities, thus hindering effective CE control measures. To overcome this limitation, this study develops an Internet of Things (IoT)-enabled framework for monitoring CEs of MiC, specifically focusing on the materialization phase, integrating across various MiC stages and facilitating detailed monitoring of CEs associated with arrangeable and reusable activities. This study builds up a CE measurement model tailored to the MiC materialization phase, providing a computational basis for a subsequent monitoring framework. By leveraging IoT technology, the framework is evaluated through case studies to confirm its feasibility and efficacy. The results indicate that the framework enabled improved monitoring capabilities, and the CEs of many MiC activities are successfully integrated into the system. The case study analysis demonstrates that the system's feedback-driven adjustments achieved a CE reduction of 732.04 metric tons.
Autonomous modular construction strategy using robotized crane based on deep learning and reinforcement learning
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.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Identifying cyber risk factors associated with construction projects
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.
The digital organization transformation (DOT) model: bridging digital transformation and organizational structures in construction firms
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.
A systematic review on safety practices in high-rise building construction: benefits, barriers, and strategic improvements
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.
Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
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.
Prediction of cost contingency in construction projects by introducing machine learning algorithms
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.
Collaborative analysis model of construction schedule and quality for RCC dam based on DES and deep learning
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.
improved generative adversarial networks
improving grey wolf optimized convolutional neural networks
Advancing civil infrastructure with digital twins: a review of applications and challenges
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 across academia, industry, and government to fully unlock the potential of DTs technology. With continued advancements in machine learning, edge computing, and secure data protocols, DTs are poised to revolutionize infrastructure management, contributing to smarter, safer, and more efficiently built environments.
Quotation impact factors for general contractors: a viewpoint from private sectors in Taiwan
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.
Evaluation model of construction quality data uncertainty: a study based on industry park project
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.
Barriers and facilitators of promoting work-related injury insurance in the construction industry: lessons from China
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.
Performance comparison of various time-series forecasting models for bridge sufficiency rating prediction
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.
Global advancements in BIM-based building e-permit system adoption: a review
Globally, several countries have begun to promote the application of building information modeling (BIM) technology and implement BIM-based building e-Permit systems. Despite the relatively rapid updates in the field of BIM-based building e-Permit systems, currently a lack of systematic sorting and summarization exists. This review evaluates the situation of seven representative countries from three aspects: the current adoption status of the BIM-based building e-Permit system, adoption background (BIM-related policy, roadmap, standards, and guidelines), and a future development plan for digital construction. These three aspects are analyzed, common measures across these countries are summarized. Four viewpoints are extracted for promoting BIM-based building e-Permit system adoption, which are mandatory policy orientation, standardization, internationalization, and intelligence. Finally, as the integration of AI into BIM-based building e-Permit systems is a recent research trend, this study also investigates existing research, highlights bottlenecks and points out future directions for AI integration.
BIM-based digital twin for the management of a railway station
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.
Evaluation of the effect of fineness of fly ash in type I and type II Portland cement blended mortars
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.
Development of an automated surface crack detection and BIM-integrated management system for concrete bridges
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.
Prediction of sewage pipeline construction duration by introducing machine learning and deep learning approaches
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.
Exploring the use of in-house sodium silicate from agro-industrial by-products in pervious geopolymer concrete
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.
Integration of BIM and ar with VSLAM to assist in construction site inspection
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.
Visual Simultaneous Localization and Mapping (VSLAM)
Sustainable design of recycled concrete using shape optimization and carbon dioxide emission based on LCA
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.
Multi-Criteria Decision Methods in the evaluation of social housing projects
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.
Modelling progressive failure of segmental linings in shield tunnels
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.
How emotional intelligence affects inter-organizational relationships in megaprojects: the boundary spanning perspective
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.
Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
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.