Journal of Construction Engineering and Management

Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems.

Abstract

Given the urgent need for decarbonization of the construction industry due to its pivotal role in global greenhouse gas emissions, industrialized construction (IC) has emerged as a promising technique to change the productivity, quality, and sustainability of construction. Although some evidence and case studies reveal that IC has distinct decarbonization advantages compared with traditional construction, there still is a need to analyze best practices, opportunities, and challenges in order to guide industry practitioners and to define key knowledge gaps. A systematic review was conducted following the three core steps: database search; research gap identification, analysis according to key lifecycle stages, and decarbonization themes. This synthesized existing academic works at the intersection of industrialized construction and decarbonization to provide a comprehensive understanding of decarbonization in IC. The findings show that although a significant amount of research focused on emissions during project stages A1–A3, there is a noticeable research gap in evaluating the carbon emissions associated with transportation, operations, and end-of-life attributes of IC. Moreover, the absence of real-time assessments during the B6–B7 operational stages impedes optimal carbon emission assessment. The verbosity of carbon estimation and tracking methodologies also adds challenges to ensuring that additional carbon impacts of IC are adequately offset by improved efficiency and lower onsite emissions. The potential of decarbonization of IC can be explored further by future research on the standardization of life-cycle assessments, development of continuous carbon-tracking methodologies, and application of alternative materials and new technologies.

Practical Applications

The effectiveness of IC practices in reducing carbon emissions may vary, as deduced from the reviewed literature. However, the conscientious selection of materials characterized by low embodied emissions can contribute to a reduction in carbon emissions, applicable to both IC and non-IC projects. IC projects are uniquely positioned to achieve an even greater reduction in carbon emissions because of a unique process that innately offers waste reduction and product quality improvements. Although a considerable volume of research has focused on estimating carbon emissions, there appears to be a gap in tracking the precise quantity of emissions across various life-cycle phases within IC. Although there is considerable research on decarbonization in prefabricated components, research on specific life-cycle phases still is lagging. Furthermore, future research should prioritize investigating similar opportunities for three-dimensional (3D) volumetric systems.

Abstract

The growing demand for sustainable and affordable construction, paired with labor shortages, requires more efficient tools and methodologies. Industrialized construction (IC) is one solution that has the ability to address construction efficiency by utilizing techniques such as modularization, construction automation, and building information modeling. One particular aspect of IC is Design for Manufacture and Assembly (DfMA) since the design phase determines up to 80% of the operating costs. However, DfMA is yet to be implemented in the construction industry due to some challenges in DfMA that inhibit full implementation. The majority of construction manufacturers have to regenerate the construction drawings for manufacturing which add substantial time to the schedule. Delays are caused by the lack of coordination of these components, as well as assemblies’ connections, compliance with building codes, and other related policies. Although many scholarly manufacturing and assembly works have studied and reviewed various aspects, techniques, and applications of DfMA, none have looked at the design process, tools and methods, or challenges in design automation. Using a systematic literature review, this study reviews the most recent innovative design responses for manufacturing and assemblies, and tools and methods. The study then analyzes the identified challenges of DfMA automation and processes in the existing literature on the construction industry, such as the lack of autonomous design and construction manufacturing. The literature analysis suggests future studies are required to improve DfMA not only as a technique but as a design strategy, particularly during the design process and prior to the production phase. Finally, future directions are provided to lead the next generation of research to advance building construction. Significantly, the results of this research provide an up-to-date status on autonomous DfMA and contribute to the body of knowledge of IC by synthesizing the state-of-the-art of building design automation and methods for manufacture and assembly.

Practical Applications

Shifting the construction work from a jobsite to a factory environment and utilizing automation are resolutions to a lack of construction efficiency. Factory automation highly relies on DfMA processes. Automating the manufacturing process itself requires advanced knowledge, particularly DfMA; which is a common term referring to both management and technology that enables designers to ease a product’s material selection, cost, and factory production. Studies have addressed the state-of-the-art for DfMA, but none have looked at the design process from the early design stage, tools and methods, and the associated challenges. This paper provides a review of design automation techniques in various thematic categories including (1) design scale, from an entire building scale to a small component like a wall, (2) design parameters like structural strengths and sustainability, (3) building material types, (4) tools and methods, and (5) the challenges for implementation. The provided paper is a fundamental resource for DfMA research in construction manufacturing. Manufacturers, developers, and builders require such a resource to learn more about various aspects of the design process and associated challenges to either improve their production lines or establish new state-of-the-art factories.

The COVID-19 pandemic had a profound effect on construction safety, intensifying the challenges faced by workers and exacerbating the industry’s preexisting safety issues. Unlike many other sectors that could shift to virtual operations, construction heavily relies on the presence and skills of workers. While previous studies have examined the effects of COVID-19 on the construction industry, little attention has been given to how the pandemic influenced workers’ safety situational awareness and safety decision-making. This research aims to study the impact of COVID-19 on construction workers’ situational awareness and safety decision-making, specifically focusing on hazard recognition (HR) and safety risk perception (SRP). The results pertaining to 67 recruited construction workers showed that COVID-19 had no impact on their ability to identify hazards. However, there was a notable difference in their safety risk perception, indicating that workers who had experienced COVID-19 had a higher level of safety risk perception. This finding can be explained by the principle of reinforced learning, which suggests that behaviors are shaped by their consequences. In other words, the experience of injuries and infectious diseases, which are less visibly linked to the occupation, acts as powerful feedback that reinforces the significance of proper perception and mitigation of risks. In an ideal situation, the primary focus should be on preventing accidents. However, if accidents do occur, as the findings of this study uncover, it is important to view them as opportunities to improve safety and prevent future incidents. This perspective acknowledges that accidents and injuries, including personal life events like COVID-19, provide opportunities that can potentially offer valuable safety lessons and increase workers’ situational awareness. Employers and workers can utilize these experiences to strive for a safer work environment. Sharing experiences and lessons learned among workers can foster a proactive safety culture, leading to continuous improvement in workplace safety.

Even though advanced heuristic and metaheuristic models have been developed for minimizing financing cost by using total float to adjust the start times of activities, these models overlook the fact that the attempt to minimize financing cost may result in more than one work schedule with minimum financing cost. It is therefore not surprising that no research has been conducted to select the best financing schedule out of the multiple optimal work schedules. It is well known that total float must be used sparingly to avoid the creation of too many critical and near-critical activities that may cause delays in the project duration. Indeed, the loss of schedule flexibility caused by the use of total float comes at a cost. It is argued in this paper that the cost of float consumption should be a vital part of models designed to minimize financing cost. The research presented in this paper attempts to fill this gap. A model is proposed in this study that not only minimizes financing cost but also demonstrates the negative impact of not considering the cost of float consumption in reaching the optimal solution, an area of research that is mostly overlooked in the finance-based scheduling literature. This research contributes to the body of knowledge by demonstrating that there may be more than one work schedule with minimum financing cost, and that the cost of float consumption must be considered to find the most optimal solution.

Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance.

Project bundling is an innovative project-delivery approach that bundles and groups different infrastructure projects into a single contract. Project bundling provides numerous opportunities and benefits for agencies and states while maintaining, rehabilitating, and repairing their infrastructure assets. Although previous research provided insights on some aspects of project bundling, none have identified the success factors needed to enhance the performance of bundled projects after the project bundling decision has been made. This study aims to fill this gap through identifying, studying, and prioritizing the success factors needed to efficiently bundle several infrastructure projects together. First, a survey was developed and distributed to numerous experts to examine the likelihood of occurrence and relative impact of 25 success factors. Second, the reliability of the survey and its results were assessed and validated using internal and external reliability assessment measures. Third, various statistical analysis tests were performed to examine the alignment between the perspectives of different project stakeholders in relation to the various project-bundling success factors. Fourth, the criticality of the different project-bundling success factors was quantified. Fifth, the success factors were ranked and prioritized to identify the key and most critical factors. The results showed that the most critical project-bundling success factors are (1) having well-defined design features; (2) having a complete project design prior to construction; (3) requiring prior knowledge or experience with similar project size and scope (by the contractor and the design agency); (4) having clear design control requirements; and (5) maintaining constant coordination and communication between all project stakeholders. This study adds to the body of knowledge by quantifying and studying the criticality of different success factors needed to enhance the performance of bundled projects. This study also provides decision-makers with the required knowledge and guidance on the key success factors that influence project-bundling performance.

Learning from prior incidents is crucial for improving safety, particularly in the construction industry where fatalities and injuries are frequent. High-precision classification of construction accident narratives is a laborious, time-consuming process that requires substantial domain expertise. However, automatic text classification had fallen short of expectations due to a lack of high-quality data sets, inadequate semantic interpretation, and primitive model architecture. To address these issues, this study developed a state-of-the-art text classification (TC) model to extract construction knowledge and classify construction accident narratives into predefined categories. The architecture of the TC deep-learning model was built based on the pretrained instruction-based omnifarious representations (INSTRUCTOR). A class-imbalance treatment (CIT) technique incorporating focal loss and weighted random sampling was embedded to make the model concentrate on hard samples and minority classes. The retrained and fine-tuned INSTRUCTOR-CIT model achieved an F1 score of 82.22% for the benchmark data set containing 1,000 accident narratives from the Occupational Health and Safety Administration (OSHA). Impressively, on a larger benchmark data set of 4,770 OSHA accident narratives labeled by another official system, the model achieved an F1 score of 94.84%, highlighting its generality. Furthermore, the experimental results demonstrated that our model was superior to existing methods with less preprocessing and higher accuracy. Finally, the contribution to construction project management was discussed to enhance unstructured data management in the construction industry. The findings of this study contribute to effective management practices and assist construction professionals focus on value-added tasks such as decision making and corrective action planning.

Wasted effort limits productivity. Successful construction project management entails identifying and mitigating causes of waste. A time-motion study was conducted to collect and classify mechanical, electrical, and plumbing (MEP) work to understand factors contributing to waste. The data were analyzed for disturbances to direct work and sources of waste within such disturbances. Installation work was disturbed very frequently. This led to low shares of direct work. Disturbances can be divided into three groups (short-, medium-, and long-term) based on duration. Short-term disturbances, involving adjusting installation areas and material handling, enabled continued direct work without waste. Medium-term disturbances included most nonvalue-adding activities within the wider work environment. They depended on installers’ understanding and required workplace adjustments. Long-term disturbances extended beyond the installation area, necessitating material searches and coordination, leading to significant waste. The study contributes to understanding MEP work and questions traditional management practices that do not provide sufficient preconditions for construction workers. Current methods are not on a sufficient level of granularity to effectively improve productivity. The study suggests integrating precise motion tracking and digital systems to reduce waste related to medium- and long-term disturbances. Providing real-time, task-specific information using digital tools can enhance situational awareness, minimizing nonvalue-adding tasks. Additionally, mobile workstations and logistical services could alleviate movement issues in such disturbances. Required granularity to accurately assess preconditions requires additional research into automating data collection and analysis.

The construction industry has witnessed unprecedented disruptions in the supply chain (SC) over the past four years due to the consecutive impacts of the 2018 steel and aluminum tariffs, the COVID-19 pandemic, and the Russian–Ukrainian war. SC disruptions have been linked to price escalations, the management of which varies across different construction contracts. Thus, there is a timely need to understand the contractual implications of price escalation clauses. This article fulfills this research requirement by following an interdependent research methodology. First, the authors analyzed and compared price escalation provisions under various US-based, international, and UK-based standard design–bid–build contracts. Second, the authors examined legal cases that have arisen due to disputes over price escalation-related matters. Third, based on the contractual and legal analysis, the authors formulated and proposed a set of contractual considerations that can be used to plan and manage the contractual implications arising from triggering price escalation clauses. The findings indicate substantial variability in contractual provisions addressing price escalation. A predominant approach entails specifying a fixed contract price, often with a general exception permitting adjustments related to legislative changes. Certain standard contracts afford flexibility through the incorporation of supplementary clauses or amendments during the contract formation stage to counteract escalation risks. Further, insights from the legal review show that judicial relief from contractual obligations is typically granted solely in instances of unforeseen and severe economic conditions. This research makes a significant academic contribution by advancing the understanding of contractual obligations concerning price escalation, especially in the context of ongoing market disruptions. Ultimately, the findings of this study will impact the construction industry by promoting more balanced contractual practices regarding price escalations in construction projects.

Construction firms face considerable challenges in relation to finding cost-effective formwork solutions to meet increased construction demands. Project stakeholders have relied on self-consolidating concrete (SCC) to speed up the construction time because SCC is highly fluid and has numerous advantages compared to traditional concrete. To withstand SCC’s high fluidity, formwork systems should be robust. Although previous research has experimentally examined various characteristics of SCC, few research studies have used machine-learning algorithms to estimate or predict the lateral pressure exerted by SCC on formwork systems. Hence, this study addressed this knowledge gap by proposing a machine-learning approach to predict the lateral pressure of SCC on vertical formwork systems. First, laboratory tests were performed to collect data on lateral pressure measurements, material factors, placement conditions, and formwork characteristics affecting the SCC lateral pressure on formwork systems. Second, four supervised machine-learning algorithms were considered in this study: $k\text{-}\text{nearest}$ neighbor (KNN), artificial neural network (ANN), decision tree (DT), and random forest (RF). Third, the hyperparameters of the machine-learning algorithms were tuned, and their performance metrics were compared. Fourth, the most accurate predictive machine-learning model was verified on an unseen testing set. The results showed that the RF machine-learning algorithm was the best model for predicting the lateral pressure of SCC on formwork systems, with a mean percentage error of 0.8%, a mean absolute percentage error of 4.29%, and a coefficient of determination R² of 0.9548. This study adds to the construction engineering and management body of knowledge by developing a machine-learning predictive model that can be used to accurately assess the lateral pressure exerted by SCC on formwork, which helps to ensure safe design of formwork systems and economic construction operations in formwork-related activities.

Despite efforts to prevent and correct unsafe behavior on construction sites, such behavior persists among construction workers. Much of what makes unsafe behavior so difficult to eradicate is its contagious nature. The contagion pattern of unsafe behavior is not only based on the group characteristics of construction workers but also influenced by government regulatory agencies (GRA) and general contractors (GC). In this study, we established a contagion model of construction workers’ unsafe behavior based on group-level factors and the dual influence of GRA and GC and employed evolutionary game theory and epidemic models to explore the contagion patterns and interventions of unsafe behavior. We find that, although the strong and weak regulation strategies by GRA suppress the contagion of unsafe behavior, the strong regulation strategy exhibits a better preventive effect; further, the active management strategies by GC have a dampening effect on the contagion of unsafe behavior and vice versa, enhanced perceived social benefits of GRA prevent unsafe behavior more than enhanced perceived image loss, and a high degree of group identity perception effectively inhibits the contagion of unsafe behavior when construction workers have relatively high levels of safety awareness. By portraying the contagion state of construction workers’ unsafe behavior in actual situations, this study provides a theoretical basis for the decision-making of GRA and GC and helps to improve the work efficiency of GRA as well as the safety management level of GC.

Work from home (WFH) connects traditional life at home and work in office (WIO) by moving work to living areas, and thus, it reshapes the time-use patterns and further influences individual greenhouse gas (GHG) emissions. However, the changes in time-use patterns and GHG emissions of the construction workforce are still unclear. This study investigated the individual time-use patterns and GHG emissions under traditional life at home, WFH, and WIO for the United States construction workforce between 2017 and 2022 based on national data sets. The American Time Use Survey data sets were applied to identify construction-related participants, extract activity information, and calculate the time-use patterns of various activities within 24 h. Individual GHG emissions were estimated by connecting the time-use patterns and GHG emission intensities of activities, which were calculated based on national Energy Consumption Surveys and the literature. The results reveal that the construction workforce reduced their leisure and sleeping time the most to accommodate the increased work and commuting needs in the transitions from traditional life at home to WFH to WIO. Aligned with the time-use patterns, individual residential GHG emissions decreased during the transitions, mainly due to cooking, laundry, and watching TV and movies, while WIO generated extra emissions from commuting and workplace activities. Overall, WIO generated 46% more GHG emissions than WFH ($7.24\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{person}/\mathrm{day}$), while traditional life at home generated 13% more emissions than WFH ($1.32\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{person}/\mathrm{day}$). The findings indicate that WFH is an environmentally friendly work arrangement in the construction industry. Several recommendations are provided to help manage activities and reduce GHG emissions in the construction industry. This study provides new insights into the impacts of transitioning work arrangements for the construction workforce from an activity-based environmental perspective and provides opportunities to promote sustainable development of the construction industry.

Abstract

The factors contributing to corruption within the construction sector have yet to be associated with the phenomenon of greed—the pursuit of self-interest and a widely recognized and extensively studied primary catalyst for corrupt practices. The mechanisms by which self-centeredness culminates in corruption within the construction industry, the prevalence of greed among the various stakeholders involved in the construction industry, and the potential impact of greed and its diverse manifestations on corruption within the construction industry remain elusive. Thus, this study analyses the diverse impacts of different manifestations of greed and self-interest on the various forms of corruption observed among construction stakeholders. The individuals involved in this study are contractors who are actively involved in the execution of building and construction projects within the geographical boundaries of Nigeria. A factor structure validity, construct validity, convergent validity, and hypothesis test using structural equation modeling were performed on the items assessed in the questionnaire. A statistically significant positive correlation was found between greed and self-interest in five specific pathways. The study found that the manifestations of corruption among contractors are subject to varying degrees of strength, contingent upon the influence of greed and self-interest. It was evident in the findings that the coexistence of biological, psychological, and social disorders contributes to an increased susceptibility to partake in corruption. The study contributes to knowledge by revealing that corruption is a sequential phenomenon initiated by greed, intensified by insatiable desires, and ultimately manifested through self-interested associations. These associations create an intricate web of influences and a complex network that enable corrupt practices within the construction industry.

Practical Applications

Practical applications of this study’s findings are in terms of understanding the multifaceted nature of corruption and designing effective interventions aimed at addressing greed and self-interest-related issues within the construction sector. The study enables an understanding of the drivers behind insatiable needs by explaining that the types and severity of corruption are inherent in the different levels of greed and the different ways in which self-interest drives individuals to engage in corrupt activities. The study’s practical application in developing corruption interventions or prevention strategies is the need to promote transparency, accountability, and integrity in both the public and private sectors by exploring the relationship between socioeconomic disparities, cultural norms, political systems, international cooperation, technology, and economic consequences. The interventions should focus on education, raising awareness about the negative consequences of greed, and promoting empathy and altruism as a means to counter excessive self-interest, and laying emphasis on the benefits of cooperation and collaboration.

Off-site steel construction (OSC) is an emerging alternative to traditional on-site methods that offers advantages such as reduced waste, improved quality, and faster delivery. However, OSC also requires robust monitoring and control mechanisms within fabrication shops to ensure the timely and cost-effective completion of projects. This study presents the development and application of a machine learning (ML) model to estimate the cost performance index (CPI), a pivotal indicator of project progress and performance, by considering influential factors that affect OSC projects. A comprehensive analysis of data from 56 OSC projects fabricated in a steel parts manufacturing facility between 2020 and 2022 was conducted. The investigation focused on four key features: project weight, project utilization type, project connection type, and material supply method, examining their impact on CPI. The data set was meticulously preprocessed, and three ML algorithms—support vector machine (SVM), gradient boosting (GB), and decision tree (DT)—were employed to model CPI. Model performance was evaluated and compared using metrics including root mean square error, accuracy, and R-squared. The findings demonstrated that GB excelled in CPI prediction, achieving an accuracy rate of 91%. This research underscores the utility of ML as a valuable tool for monitoring and controlling off-site steel construction projects. It also provides insights into the factors that influence CPI and suggests ways to optimize them for better project outcomes. Furthermore, the study contributes to the literature on OSC by exploring the relationship between project characteristics and performance indicators, which can help practitioners improve their decision-making and planning processes. The study also discusses the limitations and challenges of applying ML models to OSC data, such as data availability, quality, and consistency.

The circular economy plays a key role in the wider transition to a low-carbon built environment. Materials are critical in this transition, yet there is limited information about their current use in the built environment in many regions. This explorative study investigated circular economy in the Australian construction materials industry using a three-stage mixed-method approach. The first stage was to map actors and their practices and challenges related to material supply, use, and end of life. This involved 20 interviews with stakeholders and a workshop. The second stage was to assess key materials stocks and flows to identify the volume of the challenge through existing databases and consultation with industry stakeholders. The third stage was to understand the current state of material circularity at industry-leading examples. This was investigated through 13 interviews across two case studies, alongside site visits. These complementary research methods and stages provided findings into material circularity potential within the residential construction sector from both a macroperspective and a microperspective. The results included that new materials being used in construction are more than double the flow of waste out, showing that stocks of materials in use are growing rapidly but that data gaps are extensive. There was a focus on material reduction for cost-savings and sustainability, but there was significant opportunity for other circular economy principles, such as rethinking construction techniques. Further, in the context of a highly volatile industry, circularity requires a focus on material-specific institutional arrangements. The contribution to knowledge on construction materials and their supply chain provides fundamental insights that are required for the transition toward a circular economy, where the residential construction sector (not just in Australia but globally) would more readily reduce, reuse, recycle and recover resources, as well as being less reliant on virgin material use.

Accurate prediction of tunnel boring machine (TBM) penetration rates is of great significance for intelligent TBM construction. Traditional empirical and theoretical models of TBM penetration rates are difficult to adapt to complex and changeable formation environments. To improve the adaptability, this paper proposes a TBM penetration rate prediction model based on the particle swarm optimization (PSO)-Elman algorithm fusion. Particle swarm optimization (PSO) was used to find the optimal connection weight matrix, which was inserted into the Elman network, and the TBM penetration rate was predicted by the machine learning method. This study examined field data from two distinct tunnel sections, focusing on their geological conditions, construction challenges, and environmental impacts. By analyzing the characteristics unique to these sites, the research offers a comparative perspective on tunnel engineering in diverse settings. Five parameters—uniaxial compressive strength (UCS), rock integrity index ($K_v$), cutter head thrust ($F_n$), cutter head speed (RPM), and penetration degree ($P$)—were selected as the input parameters. The TBM penetration rate was estimated by neural network training of the model. The results show that the PSO method effectively can overcome the problem of being prone to a local minimum using the single Elman method, and the PSO-Elman model has a fast convergence speed and high accuracy. In the 20 groups of experimental samples selected, the mean absolute percentage error (MAPE) was 3.38%, and the coefficient of determination ($R^2$) was 0.936. The prediction quality was better than that of the single Elman method or the backpropagation neural network (BP) method. The study yields specific insights into efficient tunnel construction methodologies and practical neural network tools for risk management, highlighting innovative approaches in environmental preservation and safety enhancement in tunnel engineering.

Abstract

Precast construction offers multiple benefits, encompassing heightened productivity, an enhanced working environment, and significant waste reduction. Timely delivery of precast components (PCs) is of paramount importance in the successful execution of construction projects, mainly because processes involving precast prefabrication are identified frequently as critical activities in the majority of cases. However, current scheduling models for precast production need to meet the demands of dynamic environments in which construction projects contend with uncertainties. Consequently, it is essential to investigate and develop more-resilient and flexible scheduling approaches to address these challenges effectively and guarantee the timely delivery of precast components in these dynamic construction scenarios. To address these challenges, this study introduces an approach that integrates precast tracking, positioning, progress monitoring, and analysis among the multiple stages undertaken by project communities. Each component’s status is updated dynamically in the cloud-based Building Information Modeling (BIM) platform using radio-frequency identification (RFID) and ultrawideband (UWB) technologies. Moreover, the collected data are updated to a dynamic production planning engine to be analyzed more accurately and flexibly for prefabrication, fitting-out, installation resources, workerpower planning, scheduling, and execution. To verify the efficiency of the proposed system, on-site testing was performed on a prefabricated prefinished volumetric construction (PPVC) residential building project. The findings show that a precast component can be tracked precisely on-site and that the sensor network can deliver trustworthy data. This research adopted an innovative and comprehensive methodology, centering on the transmission of real-time data collected via RFID and UWB sensing to enable dynamic scheduling in precast construction. By facilitating dynamic planning, scheduling, optimization, and progress monitoring, the study introduces a paradigm shift in construction project management, markedly improving the efficiency and effectiveness of project execution.

Practical Applications

Precast construction, known for its efficiency and sustainability, faces a key challenge in ensuring the timely delivery of precast components. This is crucial because PCs assembly often represents critical activities in construction projects. Current scheduling engines struggle to adapt to the unpredictable nature of construction environments, which frequently deal with uncertainties. This study presents an innovative approach that integrates tracking, positioning, and progress monitoring of PCs using advanced technologies. By employing radio-frequency identification and ultrawideband sensors, PCs’ status is updated in real-time on a cloud-based Building Information Modeling platform. These data are uploaded into a dynamic production scheduling engine, enhancing the accuracy and flexibility of scheduling and execution, especially for prefabrication and installation stages. This approach was tested in a real-world prefabricated prefinished volumetric construction residential building project. The results demonstrate the system’s ability to locate PCs on-site precisely and provide reliable data. This holistic strategy not only allows for dynamic scheduling, optimizing workerpower and resource allocation, but also enhances overall project management. This leads to more-effective planning, optimization, and monitoring of progress, significantly improving project execution in precast construction environments.

The demolition issue of long-span spatial lattice structures has become increasingly prominent with the rapid urban development and building upgrades. Building deconstruction offers superior economic and environmental benefits compared to traditional demolition and therefore should be further promoted and applied. Dismantling in blocks is an efficient approach for deconstruction of long-span spatial lattice structures, surpassing the practice of dismantling individual components one by one. However, dividing the entire structure into substructures considering the convenience and safety of dismantling operations is the first challenge of dismantling in blocks yet to be overcome. In this study, a block planning method was proposed to aid practitioners in making high-quality block schemes. The topological relation of spatial grids defined by the adjacency matrix is the basis. The comprehensive evaluation index was established reflecting the operability, efficiency, and safety of the block scheme. Taking the combination of spatial grids as the variable and the comprehensive evaluation index combined with constraint functions as the objective, the block planning was formulated as an optimization problem. The genetic algorithm was adopted to solve this problem, and two examples were presented for verification. Results confirmed that the proposed block planning method is effective to generate the high-quality block scheme that meets topology, quantity, and lifting weight requirements and has great synthetical performance. The consideration of structural symmetry can accelerate the optimization process. The zoning planning strategy provides a tool for practitioners to make the block scheme that meets the lifting weight requirements of different zones when the crane’s lifting capacity is limited by operating space. The proposed block planning method will enrich the construction management technologies of dismantling projects and help engineers make efficient and wise decisions.

To create a resilient environment, adaptive reuse of old buildings is becoming rampant through renovation, restoration, or refurbishment. Although such efforts bring advantages by preserving heritage assets while accommodating current needs, the challenges involved in this type of project are compelling. Therefore, this study is focused on assessing the risk level of common challenges in historic building renovation and restoration projects. By conducting a survey of industry practitioners, this study identified that the common challenges have a higher average frequency than their corresponding severity rankings. Also, the survey responses indicated schematic design estimates should include the highest contingency. This study has contributed to the body of knowledge in the adaptive reuse of buildings and risk management research domain by revealing the risk level of challenges in historical building renovation and restoration projects. Moreover, this study allows industry practitioners to better manage this type of project by understanding the relative frequency and severity of common challenges.

Physical fatigue is a frequent factor in construction workplaces that threatens workers’ safety. This study examines whether fatigue alters visual search patterns adopted by workers during hazard identification, thereby affecting identification performance. Subjects ($n=24$) were exposed to four physical activity levels (nonfatigue, low fatigue, medium fatigue, and high fatigue) and then performed hazard identification tasks in a panoramic virtual reality context, with their search patterns being reflected by eye-movement metrics. The results show that physical fatigue significantly impacted the visual search patterns, with workers allocating less attention to hazardous areas and being less engaged in searching. These visual characteristics correspond to poorer hazard identification accuracy. Further, the degree of influence increased with fatigue levels. The findings could extend the knowledge of the relationship between physical fatigue and safety. They could also uncover the reasons inherent in workers’ inability to identify hazards due to physical fatigue, which could provide insight for the industry to cope with the disruptions to safety posed by physical fatigue.

Tower crane layout planning (TCLP) is key to ensure the safety and productivity of construction projects. Previous studies on TCLP mainly focused on the typical in situ construction method, however, overlooking modular integrated construction (MiC). This study aims to develop an integrated planning approach for optimizing the locations of tower cranes and trucks for MiC across the entire construction site. This approach consists of four objective components. First, identifying potential locations for cranes and trucks by adopting a gridding method and an intersection examination algorithm. Second, estimating the crane operation duration. Third, optimizing the crane and truck locations by developing an integer linear programming (ILP) model while considering various constraints, such as the demand fulfillment, the crane’s lifting capacity, and the multiple truck parking areas. Lastly, optimizing the distance between cranes to avoid crane blocking on site. The proposed approach is validated via three high-rise residential MiC projects. The results achieve 19.4%–37.9% reductions in cranes’ operation durations, as well as carbon emissions from the original plan. Compared with another existing TCLP method made from limited predefined layout plans, a 29.4% improvement is observed. Moreover, three aspects to extend the application of the approach are discussed: (1) the multiple tower crane layout scenario, (2) the impact of the number of truck points, and (3) the trade-offs between computing effectiveness and performance. The approach realizes the objective of TCLP decision-making for multi-crane multitruck MiC throughout the whole construction site. Construction practitioners can use this approach for decision-making which helps enhance the effectiveness of their tower crane layout designs.