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.
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.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
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.
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.
Is contractual flexibility always good for relationship quality among the parties in construction projects?
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.
Mapping publications on value creation in construction project settings: a mixed bibliographic and bibliometric analysis
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.
Sub-activity analysis by using wristband-type wearable health devices to measure construction workers’ physical demands
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.
Methodology for assessing and controlling the risk grade of structural columns threatened by blast incident
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.
Integrating LINE BOT and Building Information Model to develop construction information management system
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.
Measurement and spatiotemporal evolution characteristics analysis for the provincial development level of intelligent construction in China
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 gradually developing from coastal areas to inland areas. The research results provide a theoretical basis for stakeholders such as governments and enterprises to formulate differentiated development strategies for IC and also provide a reference for measuring the development level of IC and other fields in other countries.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
A relative measurement and monitoring of OTTV of glass facade passive building in tropical climate
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.
Unveiling the buffering role of prior ties in relationship conflict management in the construction industry
Relationship conflict is commonplace during the cooperation period between the general contractor and subcontractor. However, how to prevent the adverse effect arising from relationship conflict on the final project outcome is scarce, especially in empirical studies. Drawing on the conservation of resources (COR) theory, a theoretical model revealing the underlying deteriorating mechanism (relational behavior) and corresponding prevention strategies (prior ties) is developed. Based on 174 questionnaires collected from the Chinese construction industry, the model and proposed hypotheses are empirically examined. The results suggest that relationship conflict between the general contractor and subcontractor harms cooperation performance, and relational behavior mediates this relationship. If the general contractor and subcontractor have a prior cooperative relationship, the devasting impact of relationship conflict can be undermined. These findings deepen the understanding of the underlying mechanism by which relationship conflict impairs the final cooperation performance and afford insights into relationship conflict management from a pre-prevention perspective.
Exploring the impact of digital twin technology in infrastructure management: a comprehensive review
This paper examines the role of Digital Twin Technology (DTT) in transforming infrastructure management, with a focus on sustainability. It highlights how advancements in Artificial Intelligence (AI), Building Information Modeling (BIM), and the Internet of Things (IoT) are driving the effectiveness of Digital Twins in real-world applications. Through detailed case studies, the paper showcases the practical benefits of DTT across various infrastructure sectors. It also evaluates current trends and strategies for enhancing DTT integration into infrastructure systems. The research reveals a striking 80% increase in DTT-related publications from 2019 to 2024, with Asia, particularly China, leading in contributions. The paper concludes by addressing the future potential, challenges, and risks of DTT, offering valuable insights for stakeholders aiming to optimize infrastructure management in the digital era.
Exploring system boundaries for green public-private partnerships
This study addresses the research gap in understanding the intricate dynamics of public-private partnerships (PPP) in fostering sustainable development by proposing a framework delineating eight system boundaries for green PPP. Through analysis across four cases from BRICS countries, the research illuminates the nuanced and interactive nature of these boundaries. The findings underscore the necessity of recognizing green PPP as complex socio-technical systems. The paper emphasizes the diversity and complexity of green PPP’s boundaries and highlights the importance of explicitly delineating them for meaningful comparison across contexts. By clarifying these perspectives, the study contributes to enhancing academic understanding and practical implementation of sustainable development initiatives through PPP.
The impact of building information modeling on reducing greenhouse gases through design validation
This study proposes a method to reduce rework due to design errors by applying building information modeling (BIM) to reduce greenhouse gas (GHG) emissions during the construction stage. The study focuses on reducing waste in construction materials, transportation, and recycling, with the analysis grounded on expert opinions using fuzzy theory and life cycle inventory data. Applying the proposed method to the case building reduced emissions by 113,211 kg CO2eq, which is 64 times the GHGs emissions from driving a car or van for 10 or fewer passengers over 20,000 km. To offset 113,211 kg CO2eq, about 12,441–13,977 pine trees would be required. Reducing wasted concrete contributes to approximately 79.9% of the total GHGs emissions decrease. Among the buildings that started construction in South Korea between July 2022 and February 2023, 68.3% are reinforced concrete structures based on gross floor area. Applying BIM to these structures could yield even greater benefits than those reported in this study. This study also introduces a method based on fuzzy analytic hierarchy process (AHP) for decision makers to prioritize design changes. This method provides quantitative data to enrich qualitative discussions among construction, BIM, and estimation managers regarding design changes.