Construction sectors in developing countries continue to experience disproportionately high fatality rates, largely due to reactive safety practices and the limited adoption of digital safety technologies. While Building Information Modeling (BIM) and the Internet of Things (IoT) offer significant potential for proactive safety management, their integrated application remains underexplored in resource-constrained contexts. This study examines stakeholder readiness to adopt IoT-enhanced BIM-based safety monitoring systems in large-scale infrastructure projects in Pakistan, including China-Pakistan Economic Corridor (CPEC) initiatives and the Barakahu Bypass project. An integrated Technology Acceptance Model-Technology-Organization-Environment (TAM-TOE) readiness framework is employed, wherein TAM-derived cognitive-motivational factors (Technology Awareness and Perceived Benefits) and TOE-derived contextual factors (Organizational Readiness and Perceived Barriers) are examined as joint predictors of Behavioral Intention (BI). Data were collected from 107 purposively sampled construction professionals using a structured questionnaire. The results indicate high attitudinal readiness (BI mean = 4.7; perceived benefits mean = 4.6) alongside moderate organizational readiness (mean = 3.4). Regression analysis reveals that perceived benefits (beta = 0.42, p < 0.001) and technology awareness (beta = 0.29, p = 0.003) are the strongest positive predictors of adoption intention. In contrast, perceived barriers exert a significant negative effect (beta = -0.22, p = 0.022). The model explains 61.2% of the variance in behavioral intention. This study advances the literature by providing empirical evidence on stakeholder readiness for BIM-IoT safety adoption within construction management processes, estimated through a multiple regression model. It offers practical implications for policymakers and industry stakeholders seeking to accelerate data-driven decision-making and digital safety transformation in developing economies.

Rapid population growth is increasing housing demand and accelerating the expansion of the built environment in Egypt. However, practical and sustainable residential building decarbonization remains constrained by limited supplies of supplementary cementitious materials, limited structural timber resources, code restrictions on cement reduction, and cost sensitivity. This study evaluates two Egyptian multi-unit residential case studies—one affordable housing project and one middle-class housing project—to assess whether wall-system substitution can reduce both embodied and operational carbon under local material, code, and cost constraints. An integrated BIM-based digital twin workflow was used to link quantity takeoff, finite-element structural assessment, and whole-building energy simulation. An architectural BIM model was used for material quantification, wall-system definition, and energy-model inputs. A structural model was used to assess the effects of reducing wall density on reinforcement and concrete demand under gravity and seismic load combinations. Operational performance was assessed through cooling-focused energy simulations under hot-arid climatic conditions representative of Egypt’s new desert cities. Alternative wall systems were then evaluated through scenario- based material substitution and revised structural and energy assessments. The results show that reinforcement, concrete, and wall- core materials account for about 80% of total embodied carbon, while cooling accounts for about 72% of operational emissions. Non-structural cement uses, mainly mortars and finishes, account for 36% of total cement demand, ranging from 161 to 229 tons per building across the two case studies. Replacing conventional partition walls with lightweight, energy-efficient alternatives reduced embodied carbon by up to 35.2%, operational carbon by about 15.7% to 16.5%, and total life-cycle carbon by about 17.4% to 17.5% over a 60- year service life. The average savings per building corresponded to avoiding about 30 tons of steel, 165 m3 of ready-mix concrete, and 191 m3 of mortar, with net cost savings of about 3.15 million EGP per building. These results identify a practical pathway toward more sustainable, lower-carbon Egyptian residential buildings without increasing project cost.

Currently, China’s construction industry is evolving towards green, low-carbon, and intelligent development, with prefabricated construction playing a crucial role in this transformation. However, existing risk research on prefabricated residential projects remains fragmented, focusing on isolated risk factors or specific construction stages, and lacking systematic analysis of risk interactions. To address this gap, this study introduces vulnerability theory and develops a structural equation model (SEM) to quantitatively analyze risk factors. Through literature review, case analysis, and expert surveys, 28 vulnerability risk factors were identified and categorized into exposure, sensitivity, and adaptability. A SEM was constructed and validated using 365 valid survey responses. The results reveal that resource deficiency, human factors, and lack of management capacity exert the strongest overall impact on project risk, with path analysis further demonstrating how these vulnerability dimensions propagate risk through interconnected pathways. This study makes two key contributions: First, it extends vulnerability theory to prefabricated residential construction through a three-dimensional analytical framework; second, it demonstrates the integration of vulnerability theory with SEM, offering a replicable quantitative approach. The findings provide project managers with a diagnostic tool for identifying systemic weaknesses and prioritizing interventions.

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China-Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology-Organization-Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts.

Prefabricated buildings have become important in the transformation and upgrading of the construction industry due to their advantages, including high efficiency, energy conservation, low cost, and environmental friendliness. To further promote the wide application of prefabricated construction, the improvement of construction organization design has become an urgent problem to be solved. Therefore, this study developed a new evaluation method for prefabricated construction collaboration. The proposed evaluation system was built based on the combination of knowledge- and data-driven approaches, i.e., a dual-driven evaluation method. The knowledge-driven part of this evaluation system used an evaluation model based on the analytic hierarchy process (AHP), while the data-driven part used a prediction model based on the BO-XGBoost algorithm to verify the validity of the AHP-based model. To demonstrate the effectiveness of the proposed dual-driven evaluation system, we conducted a case analysis using the data of 204 construction cases obtained from digital simulation platform experiments. The results of the AHP-based evaluation model showed that there was a significant disparity in construction collaboration levels in this case study, with a large proportion of low-level collaboration cases. This indicated that there was a lack of proper collaboration in project management, component production, and on-site assembly, reflecting the urgent need for improvement in collaboration efficiency. Regarding the data-driven analysis, the BO-XGBoost prediction model was built based on the AHP-based evaluation results. It was found that the prediction accuracy of the BO-XGBoost model was as high as 98.1%, indicating that the proposed AHP-based model was scientific and effective. Moreover, the BO-XGBoost model was compared with the random forest, support vector machine, and logistic regression prediction models. The BO-XGBoost model outperformed the other three prediction models in terms of accuracy, precision, recall rate, and F1 score. The proposed dual-driven evaluation system provided a new perspective for the scientific evaluation of prefabricated construction collaboration. The findings of this study contributed to enhancing the project management optimization capability of smart construction sites.

The construction industry in Pakistan faces persistent challenges due to uncertainties such as behavioral intention, risk identification, and stakeholder perception, which often lead to significant losses in construction activities and human resources. This study aims to quantitatively evaluate these critical factors within the theoretical framework of Building Information Modeling (BIM) and the Technology Acceptance Model (TAM). Specifically, key constructs—Behavioral Intention (BI), Hazard Identification (HI), and Stakeholder Perception (SP)—are analyzed to assess their influence on construction safety management practices. A structured questionnaire was distributed electronically to construction professionals across various ongoing projects in Pakistan. The questionnaire items were based on a five-point Likert scale, and reliability was confirmed with high Cronbach’s alpha values for BI (0.82), HI (0.92), and SP (0.91). To evaluate the relationships between constructs, descriptive statistics and multiple regression analysis were employed. The regression results showed strong model fit for BI and HI (R2 = 0.945), and near-perfect fit for SP (R2 = 0.998), demonstrating robust predictive power. Significant correlations were found among independent variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), and others. This study further identifies Trust (TR) and Organizational Culture (OC) as critical predictors of stakeholder perception in the BIM context. A conceptual framework was developed incorporating statistical parameters (e.g., p-values, R2, t-stats) to categorize the effectiveness of BIM and TAM theoretical integration for safety risk management. This approach is novel in its use of TAM-based constructs to evaluate BIM-related safety outcomes in the Pakistani construction sector—a context where such empirical evidence is limited. The findings provide predictive insights into how behavioral, perceptual, and organizational variables influence construction safety performance, offering practical implications for BIM adoption and safety policy design.

IntroductionThis study explores the impact of Building Information Modelling (BIM) staff on construction project performance, with a focus on the roles of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF). The research aims to understand how BIM personnel attributes influence project outcomes and how these effects are mediated by TTF and moderated by UTAUT.MethodsPartial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the data, allowing for the examination of complex interactions between BIM staff attributes, project characteristics, and outcomes. This method is particularly suited for handling smaller sample sizes and non-normal data distributions.ResultsThe analysis revealed that BIM staff attributes—such as team size, expertise, training, and experience—significantly influence construction project performance. Key performance areas affected include design efficiency, error reduction, and adherence to schedules and budgets. TTF emerged as a critical mediator, enhancing performance metrics like stakeholder satisfaction and cost management. UTAUT further moderated the relationship between BIM staff, TTF, and project performance, highlighting the importance of technology acceptance and use within BIM contexts.DiscussionThe findings emphasize the need for organizations to strategically allocate BIM staff and invest in their professional development to optimize project outcomes. Recommendations include fostering supportive organizational structures and promoting a culture of collaboration and innovation to enhance project resilience and performance, particularly in the face of disruptive events. Future research should expand to diverse project types and scales, investigate emerging technologies, and consider cultural factors through cross-cultural studies. Longitudinal studies and cost-benefit analyses of BIM training and technology adoption are also suggested to provide deeper insights and inform strategic decision-making.

Facing the massive amount of risk management data for deep foundation pit excavation (DFPE) construction, how to store and utilize these data to prevent the occurrence of risk events is an urgent problem that needs to be solved. The goal of this research is to build an ontology knowledge base aimed at standardizing and formalizing risk knowledge in the field of DFPE construction risk. The ontology knowledge base can promote construction risk identification and reduce the occurrence of risk events. A six-step method was applied to define the ontology classes and class hierarchy, determine objects and related data attributes. Ontology instances were created to further evaluate the integrity, correctness and consistency of the developed knowledge base. The case studies showed that through the formalization of knowledge and establishment of rule libraries, rule reasoning could intelligently identify potential risk events and provide risk prevention measures. This research contributed a new perspective to resolve problems of access and integration knowledge and information in DFPE risk management work. By structuring and standardizing knowledge and information, the ontology-based risk knowledge base can facilitate knowledge transfer, sharing and reuse among different project participants. The ontology knowledge base can provide decision support for managers to protect the on-site workers, building structures and the surrounding environments.

The building industry significantly contributes to global warming, driving the demand for sustainable construction and green buildings. However, barriers like cost concerns and limited knowledge persist. Previous studies have used multi-objective optimization (MOO) to minimize life cycle cost and environmental impact, often emphasizing energy efficiency. In equatorial climates, unique factors like material selection must be considered. This study assesses the cost-effectiveness of sustainable materials, focusing on envelope materials in Ecuador. The case study is a single-family house in the equatorial climate, optimized using Building Information Modeling (BIM), Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA). In this study, a MOO process using the weighted sum approach (WSA) identifies sustainable house designs. The sustainable houses achieve a 98% decrease in Ozone Depletion Potential, a 75% reduction in Global Warming Potential, and a 45% drop in Primary Energy Demand, although they still incur a 30% increased cost. The results offer a foundation for cost-effective, eco-friendly housing solutions. Bamboo emerges as a promising material with local acceptance. This research highlights the significance of material selection in sustainable construction and provides a replicable approach for diverse settings. It aims to promote sustainable housing solutions in Ecuador and beyond.

Large-scale complex public projects face greater, more uncertain, and more diverse risks due to their particular characteristics of large size, long duration, and complexity, which bring both opportunities and challenges for construction enterprises. At present, the research on the schedule risk of large-scale public projects mainly focuses on identifying risk factors causing schedule delays and predicting such delays. However, there is a lack of comprehensive analysis of the interactions among risk factors, and quantitative assessments of the impact intensity and combined effects among these factors are not conducted. Additionally, the use of Bayesian networks (BNs) for quantitative risk assessments faces challenges such as unclear causal and dependency relationships among factors in complex systems and insufficient model interpretability. The purpose of this study is to introduce interpretive structural modeling (ISM) to analyze the relationships among risk factors at various levels, thereby facilitating the construction of a BN for predicting delay risks in large-scale and complex public projects. This approach aims to effectively address the prevalent issues of incompleteness and uncertainty inherent in such projects. Furthermore, based on this foundation, the study delves into the quantitative relationships and integrated effects among risk factors. Initially, a comprehensive methodology is employed to establish a comprehensive set of schedule risk factors. Subsequently, ISM is utilized to analyze the hierarchical relationships among these factors, which in turn supports the modeling of a BN to construct a schedule delay risk assessment model. This model enables the prediction and quantification of schedule risks, which are then validated through real-world project cases. Finally, a thorough examination of quantitative relationships, such as factor importance, sensitivity, and integrated effects, is conducted. The results demonstrate that the established model exhibits high accuracy, reusability, enhanced interpretability, and credibility, significantly improving the prediction of schedule delays in practical engineering projects. Moreover, it clarifies the quantitative relationships among the risk factors contributing to schedule delays, providing a scientific and effective theoretical basis and control tool for schedule risk management.

The construction sector of Pakistan is on a cross-growth trajectory, developing under the twin pressures of emerging infrastructure-based demands and sustainable practices that need to be inculcated urgently. This article focuses on the critical evaluation of sustainable waste management practices within the fast-developing construction industry of Pakistan, and clearly delineates a research gap in the current methodologies and use of data combined with the absence of a strategy for effective management of concrete waste. This research aims to utilize an algorithm based on machine learning that will provide accurate prediction in the generation of construction waste by harnessing the potential of real-time data for improved sustainability in the construction process. This research has identified fundamental factors leading systematically to the generation of concrete waste by creating an extensive dataset from construction firms all over Pakistan. This research study also identifies the potential concrete causes and proposed strategies towards the minimization of waste with a strong focus on the reuse and recycling of the same concrete material to enhance the adoption of sustainable practices. The prediction of the model indicates that the volumes of construction are to increase to 158 cubic meters by 2030 and 192 cubic meters by 2040. Further, it projects the increase in concrete construction waste volumes to 223 cubic meters by the year 2050 through historical wastage patterns.

Fifth generation (5G) New Radio (NR), was developed to offer more flexibility to meet new service requirements. Meanwhile, machine learning (ML) has proven successful in a variety of tasks, such as natural language processing, computer vision, and pattern recognition, in particular, which is proven to have a performance that is proportional to the total amount of available data. In NR, the capability to locate users is still one of the critical obstacles when mobile operator is planning and optimizing the cellular networks. Developing the technique to distinguish indoor from outdoor users' traffic pattern can achieve higher efficiency in terms of resource management and which results in larger economic benefit. In this paper, we present a pattern classifier based on decision tree to solve the indoor/outdoor classification problem. More specifically, rules for classification of indoor/outdoor users are generated by repeatedly splitting the features from cellular network key performance indicators (KPIs) which utilize the measurement criteria of entropy from the information theory community.

The current research on the schedule delay risk of large public projects mainly focused on identifying the risk factors of schedule delay and analyzing the interaction between the risk factors, there was a lack of quantitative analysis of the sensitivity and importance of risk factors and the intensity of influence between risk factors. In this study, Bayesian network and interpretive structure model were used in the assessment of schedule risk of large and complex public projects. Based on the collection of 7 deferred risk factor systems derived from real cases, 24 major deferred risk factors were refined, then the explanatory structure model was used to process the risk factors into four different levels, and the hierarchical structure diagram was transformed into a Bayesian network. Finally, according to the established Bayesian network, the risk factors were collected and evaluated based on actual project data. The collected data were imported into GeNIe, the parameters of the Bayesian network model was learnt, and obtain the probability distribution of the risk level of schedule delay based on the sample data.And using Bayesian network reverse reasoning, sensitivity analysis, and influence intensity analysis were used, the key factors and sensitive factors that lead to schedule delays in large and complex public projects are clarified, and a scientific and effective theoretical basis and control tools were offered for schedule risk management.

建筑设计合规性自动检查对保证建筑信息模型(BIM)符合设计规范要求,增加规范检查自动化程度具有重要意义.结合合规性检查理论与专家系统方法,提出了以BIM模型为检查对象的合规性自动检查系统框架,以规则知识与推理机制分开的方式实现合规性检查过程.以《住宅设计规范》为例,对规范中的条文进行知识分析,总结出规范知识表达式,构建规则库和规则库访问机制;建立了逻辑策略下推理机制,将规则库中的规则信息与BIM信息进行推理,输出检查结果;最后构建了合规性检查系统验证平台,通过BIM模型实例完成模型数据提取及规则推理的过程,实现了合规性检查的功能,验证了合规性检查方法框架.该方法在一定程度上能够指导后续的合规性检查相关研究,有效提高BIM模型的建筑设计合规性检查效率,保证检查质量,促进建筑工程领域信息化的发展.

本文对提高BIM模型正向设计的规范性和准确性,保证BIM模型作为设计成果递交的可靠性具有重要意义.论文介绍了专家系统在合规性检查中的应用,分析了建筑设计规范知识获取和表达,建立了建筑设计规范规则库,提出了BIM模型信息提取与映射的基本方法.在此理论基础上提出了合规性检查系统基本框架,开发了基于BIM模型的建筑设计合规性检查原型系统,并通过实际案例验证了系统的理论性和可行性.最后,论文结合今后合规性检查智能系统的发展,针对模型交付标准和规范条文定义两方面提出了建议.

The BIM component is an important BIM resource in BIM application process of construction enterprises, which reflects the maturity and competitiveness of BIM application of an enterprise. The systematicness and completeness of BIM component library play a very important role in the creation and application of BIM model in construction enterprises and the efficiency of BIM modeling. Currently, construction companies are always lacking of standardized BIM component management, facing with many difficulties of low standardization of BIM components, high cost of software and hardware, lack of support of BIM component management platform, and etc. By standardizing the management methods and processes of BIM components, the study in this paper analyses the functional and performance requirements of BIM component library system, completes the main function design of BIM component library management system, and establishes BIM component library management system of construction enterprise based on public cloud, which provides a feasible technical route for enterprise BIM component library management.

BIM技术已经深入到我国建筑领域的各个方面,但是各专业之间的合作效率依然无法提高,其主要原因是缺乏统一的信息交换标准以及集成的协同工作平台.本文通过阐述IFC/IDM/MVD开发方法及创建流程步骤,以buildingSMART组织提出的国际标准格式,对MVD的Diagram,Concept,Document三大模块进行了视图化实现,使进度管理过程中信息传递更加流畅;开发了工程项目进度管理阶段的BIM应用流程,并通过流程图的信息交换过程定义了信息交付手册(IDM)中相应的信息交换需求;将IFC标准数据格式绑定模型的信息交换需求,对MVD的各个概念进行模块化描述,使基于此模型开发的BIM应用软件可交互使用,保证了语义与逻辑的一致性;最后验证了绑定IFC子模型的信息内容,并整合到mvdXML格式的IfcDoc工具中,为基于IFC的工程项目进度管理信息系统的开发提供了依据.

In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge.

随着建筑信息模型的规模和复杂性不断增加,利用单台计算机处理海量BIM数据的存储和分析变得越来越困难。传统的关系数据库、面向对象数据库等已经无法满足当下建筑业海量和多样化的数据存储和管理的需求。而大数据技术的出现为建筑信息模型海量数据的存储、管理和分析带来极大的潜力。利用大数据技术管理BIM结构化和非结构化数据的优势,探讨分布式大数据平台Hadoop和HBase数据库整体架构和存储模型;制定基于HBase数据库存储IFC(工业基础类)结构化数据和非结构化数据的策略及数据表格的设计;建立基于Hadoop和HBase大数据环境的建筑信息模型存储系统,实现对IFC数据的基本管理操作。通过实际案例验证该系统的可行性。

IFC standard defines the data format of BIM which organizes and manages building modeling information by using spatial structure,and describes building information by EXPRESS language.The IFC standard contains numerous entities with complex relationships and multiplied logical levels.Therefore,the standardization and structured management of the construction information is the key to the implementation of IFC standards.This paper analyses the spatial structure of the IFC standard and the corresponding IFC neutral file,resolves the IFC file with Java plug-in,and explores the spatial structure of the IFC model and the building components by using Java.Finally,this method has been evaluated by a detailed constructional engineering project.