This paper takes the energy-saving benefits of intelligent construction technology in green buildings as the core, and systematically researches the application effects of building information modeling (BIM), IoT-driven building energy management system (BEMS), and energy consumption dynamic anomaly detection model. Three-dimensional visual modeling of building structure is realized through BIM technology, which solves the problems of structural stress, material optimization and site adaptability, and improves design safety and stability. The BEMS system is constructed based on the three-layer architecture of the Internet of Things, realizing real-time monitoring of energy consumption data and driving refined energy management and control. For massive energy consumption data processing, an approximation sampling model is proposed to determine the optimal sample size through iterative calculations, and the K-center clustering algorithm is used to dynamically detect energy consumption anomalies. Taking an office building as a case study, the energy consumption data of 100 consecutive days is extracted based on the IoT BEMS platform, and the energy consumption anomaly detection model is constructed by analyzing the dimensionality reduction. The power consumption on July 2 is detected to surge to 9124.16 KW with an anomaly score of 2.509, which is significantly higher than the normal threshold, while the power consumption on the remaining days is stable at 2462-3844 KW (anomaly score \(\mathrm{\le}\) 0.409). The verification of energy-saving effect shows that the system realizes energy consumption of 2423.29 KW on July 1 by dynamically regulating the end-end equipments, saving 1420.97 KW of electricity on a single day compared with 3844.26 KW of the traditional long-light and long-cooling mode, and the energy-saving rate reaches 36.96%.

This paper explores the adoption barriers to automation in the Saudi Arabian’s construction industry, from the perspectives of contractors, consultants, and owners. Eighteen barriers were uncovered through literature review and pilot-testing. A questionnaire survey was designed and presented to practitioners in the construction sector before distributing to the targeted professional. Ninety-five responses were analyzed utilizing the Relative Importance Index (RII). Cronbach’s alpha tool was employed for examining the reliability of the collected data. The agreement level among the stakeholders was determined. The analysis shows that there is moderate agreement among the stakeholders. The top barriers of adopting automation are “High initial capital cost of automated equipment”, “Shortage of fund in automation research and development”, “Individuals’ resistance to change”, “Shortage of skilled workforce”, “Lack of knowledge in automation applications”. This research contributes to expand the boundaries of knowledge in the field of construction automation through presenting the barriers that should be taken into account by researchers and practitioners in developing countries, prior to implementing automation, so that future solutions can be investigated. The finding can benefit authorities in setting plans, developing strategies and introducing incentive programs to encourage the primary stakeholders in the construction industry to adopt automation.

Building Information Modelling (BIM) is reshaping the global construction industry by enhancing collaboration, transparency, and sustainability throughout the project lifecycle. In Romania, however, BIM implementation in publicly funded construction projects faces significant legal, institutional, and cultural challenges. This paper examines the current regulatory landscape, focusing on national strategies, public procurement laws, and relevant technical documentation frameworks. Drawing on recent national surveys and case studies, it explores industry perceptions, technical barriers, and the level of BIM maturity. The findings reveal a growing awareness and gradual adoption of BIM, yet highlight persistent obstacles such as limited professional training, unclear legal responsibilities, and high implementation costs. Based on this analysis, the paper provides strategic recommendations for improving the regulatory framework, developing national standards, fostering interdisciplinary education, and accelerating digital transformation across the Romanian construction sector in alignment with EU policies and international best practices.

The work performance of housing agents is key to enhancing the profitability and sustainable development of the real estate brokerage industry. An outstanding performance by housing agents translates to more sales, increases the agents’ income, and helps them achieve organizational objectives. Thus, this study introduced transformative leadership and proactive personality traits as extrinsic factors and mediator variables into a structural framework on the causal relationships among work engagement, work meaningfulness, and job performance. Structural equation modeling was used to analyze the data collected through online and in-person questionnaires. A total of 860 questionnaires were recovered, of which 548 were valid, indicating an effective response rate of 63.7%. The empirical results revealed that transformative leadership significantly and positively influences job performance through work engagement. In addition, proactive personality traits significantly and positively influence job performance through the work meaningfulness as a mediator. Therefore, this study highlights the importance of transformative leadership and proactive personality traits as forerunners of the work meaningfulness and work engagement that influence job performance.

As the complexity of construction projects increases, traditional cost management methods are difficult to meet the demand for accurate control. The combination of multi-intelligence body optimization algorithm and BIM technology provides a new solution path for construction cost management. The study constructs a multi-intelligence body reinforcement learning model through Markov process and time series differential learning, develops a building cost framework deepening design plug-in to realize BIM automated modeling, and proposes annotation collision detection and intelligent annotation methods based on hybrid enclosing box. The results show that the application of BIM technology reduces the building cost consulting cost from 112,872,000 yuan to 60,630,000 yuan, saving 46.3%; the comprehensive benefit score of BIM technology application for the assembly building project of ZX Middle School reaches 83.0945 points, which is in the evaluation range of “comparatively good”, and the construction management benefit score is the highest, which reaches 96.0465 points. The study concludes that the combination of multi-intelligent body optimization algorithm and BIM technology can effectively improve the accuracy of construction cost control, reduce project cost and enhance the level of building design intelligence.

Aiming at the problems of insufficient feature extraction of small targets in the construction defect detection of urban underground cable intermediate joints, a detection algorithm based on improved YOLO11 is proposed. A panoramic imaging framework is implemented through multi-camera collaborative acquisition and SIFT-based image stitching, effectively resolving the issue of defect omission in single-view imaging. Key enhancements to YOLO11 include the integration of deformable convolution (DCNv2) to improve geometric adaptability for modeling misaligned semiconductive layer stripping defects, the incorporation of large kernel attention (LSKA) to strengthen global contextual awareness of construction anomalies, and the addition of a P2 small-target detection layer to refine localization accuracy for main insulation contamination, scratches, and burrs on compression sleeves. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 80.3% and an mAP@0.5 of 70.3% for four typical defect categories, representing improvements of 4.1% and 16.6%, respectively, over the baseline YOLO11s. The algorithm outperforms mainstream lightweight models such as YOLOv8s and YOLOv10n, providing a high-precision automated solution for quality inspection of urban underground cable joints.

The future of student housing in developing countries is increasingly being defined by a transition towards smart and sustainable student accommodation, a shift which is relevant to the higher education institutional student residences of South Africa. The aim of this paper is to explore global research trends in relation to new models of student accommodation, with particular emphasis on the integration of renewable energy, smart housing technology, and sustainable building methods. Using the Elsevier Scopus as the primary database, bibliometric analysis and network visualisation (VOS viewer) were applied to 38 retrieved publications between 2014 and 2025. The search combined the following terms, (“student residence” OR “student housing” OR “university accommodation” OR “smart housing” OR “sustainable housing”) AND (“IoT in housing” OR “smart campus” OR “renewable” AND “energy”). The findings identify six primary thematic clusters: (1) Low-Carbon and Energy-Efficient Building Systems, (2) Economics and Policy of Sustainable Urban Housing, (3) Design Innovation and Renewable Integration, (4) Zero-Energy Housing and Educational Integration, (5) Climate-Responsive Housing and Emissions Mitigation, and (6) Retrofitting and Material Strategies for Low-Energy Housing. The main challenges that are indicated are policy misalignment, budgetary constraints, and technological adoption barriers, which limit the attainment of net-zero and climate-resilient student accommodation. The study limitations are the exclusion of non-English literature and a single bibliographic database being used. The paper contributes to literature by providing a comprehensive, evidence-based synthesis of global research trends and making strategic policy recommendations for policymakers, academic scholars, and industry stakeholders that prioritize bringing holistic energy, design, and policy innovations to improve the sustainability of student housing.

This study explores the drivers and challenges for utilizing the Internet of Things (IoT) in the Saudi Arabian facilities management (FM) Industry. A literature review identified IoT applications, drivers, and challenges in FM, followed by a survey of 60 practitioners (30 facilities managers and 30 IoT specialists) on IoT utilization in the Saudi FM industry. The analyzed results informed recommendations to enhance IoT adoption. The common drivers between the two groups of survey participants (with frequencies of 50.0% or more) included cost saving by optimizing energy usage, improving maintenance schedules and reducing downtime; and improving the efficiency of operations by providing real-time data on systems’ performance and usage patterns. The common challenges between the two stakeholder groups (with frequencies of 50.0% or more) included the lack of skills and knowledge about new technologies in FM organizations; and the inability to integrate legacy FM systems with IoT devices and platforms. This research contributes to the FM literature and industry by exploring various knowledge domains within a highly relevant and demanding area of the profession. The study’s outcomes offer potential for enhancing the current level of IoT adoption in Saudi Arabia’s FM industry. This is the first study that provides an analysis of the drivers and challenges for IoT utilization in the Saudi Arabian FM Industry. The outcomes provide a potential for improving the current level of IoT adoption.

During the installation of urban underground cable intermediate joints, errors made by construction personnel frequently lead to defects, including unpolished burrs on the connecting tube, uneven stripping of the outer semi-conductive layer from the main insulation, and scratches or stains on the surface of the main insulation. To mitigate these defect hazards and enhance the stability of cable operations, this paper presents a defect detection method that integrates the U-Net semantic segmentation algorithm with the Canny edge detection algorithm. First, the U-Net algorithm is utilized to segment the intermediate joint image into three components: main insulation, outer semi-conductive layer, and crimping tube. Subsequently, the Canny algorithm is employed to extract the edge information from the images of the crimping tube, main insulation, and semi-conductive layer. The edge curve is then fitted and analyzed numerically to determine the presence of burrs or uneven stripping defects. Finally, to assess the stains and scratches on the main insulation surface, the number of edge points in the connected regions is utilized to ascertain the existence of defects. Experimental results demonstrate that the integration of the U-Net and Canny algorithms effectively identifies various defects, achieving a detection accuracy exceeding 90%. This method effectively mitigates the latent operational hazards posed by construction defects in urban underground cable intermediate joints and significantly enhances the stability of the power system.

The building contract administrator (usually the architect) is assigned to all the project matters from the conceptual stage till the completion of the project. As the number of projects is increasing and the scope of the building contract administrator is huge, architects are unable to hands-on every project and they require the involvement of graduate architects to assist in their work. Previous research showed that architectural firms are dissatisfied with the quality of graduate architects, and they have to re-train to make them fit for their jobs. Hence, the purpose of this study is to enhance the performance of graduate architects in building contract administration (BCA) through the development of a BCA checklist for graduate architects’ self-improvement. This study adopted a qualitative method where 7 housing projects in Klang Valley were selected as case studies. From the case study, 11 types of documents were reviewed and 20 respondents were selected for semi-structured interviews. Data collected were analyzed using content and thematic analysis. This study serves as a reference tool for graduate architects’ professional development in BCA. Academics can use the outcome of this study as a reference in their teaching modules to help students think about complex situations of BCA.

The 2030 Agenda is all about inclusiveness and gender balance. The building industry is key to achieving many Sustainable Development Goals (SDGs), including Goal 5 (gender equality), but the sector is male-dominated. Research about closing the gender gap in building skilled trades, especially in young-adult developing countries’ informal sector, is scarce. Thus, this research investigates barriers and suggests ways to help young adult females embrace building skilled trades as careers in the informal private sector and, by extension, improve their achievement of Goal 5. The study adopted a qualitative research design. The study data were collated through semi-structured interview questions. The study covered selected cities across Nigeria and achieved saturation at the 35th Interviewee. The researchers adopted a thematic technique to analyse the collated data. The findings identified cultural and religious issues, early marriage, lower pay, men-dominated sector, unregulated sector, bullying, and lack of mentors/role models as the perceived major barriers facing Nigerian construction young-adult females’ building skilled trades gender equality. Achieving Goal 5 may become an illusion if these barriers are not mitigated. As part of the study’s originality, the research recommends a multi-dimensional, all-inclusive mechanism to bridge the gender inequality gap in Nigeria’s skilled construction trades, especially in the informal private sector.

This study uses a social capital approach to examine the differences in housing prices between non-arm’s length and arm’s length transactions. It employed propensity score matching to generate comparable matched pairs and eliminate selection bias, thus facilitating the discernment of housing price differences between non-arm’s length and arm’s length transactions. It used the actual housing prices registered from January 1, 2012, to December 31, 2019, in Taipei City and New Taipei City. The data were divided into two groups: a treatment group of non-arm’s length transactions and a control group of arm’s length transactions. The empirical results indicated that transactions involving first-, second-, and third-degree relatives were priced 53.3%, 51.4%, and 47.2% lower than arm’s length transactions, respectively. Urgent buying/selling transactions were priced 21.7% lower than arm’s length transactions. Transactions influenced by obligations, liabilities, or debt settlements were priced 12.0% lower than arm’s length transactions. Purchases from government agencies were priced 45.1% lower than arm’s length transactions. Transactions between friends were priced 26.3% lower than arm’s length transactions. These empirical results indicate the importance of non-arm’s length transactions on housing prices, with significant rebates due to implicit social capital.