Construction Management Research Articles

INVESTIGATING THE RELATIONSHIP BETWEEN TECHNOLOGY INTEGRATION AND CONSTRUCTION MANAGEMENT EFFICIENCY IN PROJECT DELIVERY

The integration of technology in the construction industry is reshaping project planning, execution, and management, driven by increasing demands for efficiency, sustainability, and quality. This study investigates the relationship between technology integration and construction management efficiency in project delivery, particularly within the Nigerian construction sector. A quantitative approach was employed to gather data from 195 construction professionals in Lagos State via a questionnaire, resulting in a response rate of 76%. The results shows that construction professionals revealed that BIM was regarded as the most effective technology, significantly improving collaboration and error reduction. The benefits of technology extend to improved quality control and client satisfaction by reducing errors and ensuring adherence to standards. It also improved project scheduling and timelines, and enhanced collaboration and communication. Data was gathered exclusively from construction professionals based in Lagos State, Nigeria. To gain a more comprehensive perspective, it is suggested that future studies include a greater number of states and a wider range of construction firm categories. The Nigerian construction industry can apply the recommendations to enhance technology integration on-site. Additionally, the government can utilize these suggestions to create policies that encourage the adoption of technology in construction for more efficient project delivery. Technology integration in the Nigerian construction industry is still in its developmental phase. Through this article, the author adds to the existing body of knowledge in this area of research.

EDITORIAL

Dear All, Assalamu'alaikum warahmatullahi wabarakatuh. Journal of Architecture, Planning and Construction Management (JAPCM), Kulliyyah of Architecture and Environmental Design is one the official journals of International Islamic University Malaysia (IIUM), under IIUM Press. It embarked in 2011 and is dedicated to the publication of original articles on the specialized fields of Architecture, Planning, Landscape Architecture, Quantity Surveying, Building Technology and Engineering, Applied Arts and Design, Construction Management and those related to the Built Environment. JAPCM is also the ardent forum for the reports of research that bridged the Built Environment and the Islamic worldview. Prof. Ar. Dr. Abdul Razak Sapian Editor-in-Chief

Sustainable resource management in construction: Computer vision for recognition of electro-construction waste (ECW)

Sustainable resource management in construction: Computer vision for recognition of electro-construction waste (ECW)

Impact of Automated Grade Control in Crawler Dozers for Job Sites in India

The absence of skilled operators poses a growing challenge, particularly evident in crawler dozers, where less experienced operators often encounter issues such as the "washboarding" effect, where grading errors compound with each pass. This effect stems from various factors, exacerbated by poorly balanced machines. This study focuses on exploring diverse autonomous grading technologies in dozers and their benefits, especially concerning the demand in India for tasks like road construction, site preparation, land clearing, field leveling, and landfill management activities. Research indicates that machines equipped with 2D and 3D grade control systems exhibit significantly higher efficiency, up to 2 to 3 times more productive than crawler dozers lacking grade controls, depending on the operational requirements. Leveraging advanced dozer technologies such as GPS-guided systems for precise grading, telematics for remote monitoring and fleet management, and automation features enhances operational efficiency, reducing operator inputs by up to 80% [1]. Adopting these technologies enhances operational efficiency and provides machine owners with increased productivity, cost savings, and improved job site performance. This paper also focuses on the procedure to convert a traditional machine into a smart machine by deploying the grade control third-party solutions. It has been observed that with the implementation of the grade control system, the productivity of the crawler dozer improves by 69% for a highly skilled operator. Additionally, the customer benefits in terms of the amount of material moved per liter of fuel and cost, which is elaborated on in this paper. Keywords: Automated Grade Control, Crawler Dozers, Job Sites, Control System, Dozer Productivity, Skilled Operator

Numerical modelling of the impact of drainage system clogging on urban flood processes.

Numerical modelling of the impact of drainage system clogging on urban flood processes.

Prioritizing Mental Health to Improve Academic Performance and Enhance Construction Engineering and Management Students’ Well-Being

Prioritizing Mental Health to Improve Academic Performance and Enhance Construction Engineering and Management Students’ Well-Being

Influencing turnover intention among building information modeling (BIM) Workers in China: A structural equation modeling approach.

Influencing turnover intention among building information modeling (BIM) Workers in China: A structural equation modeling approach.

Machine learning-based equipment sound classification for advanced construction management and site supervision

This study focuses on machine learning classification of the sounds of equipment operating in the construction site environment to support improved construction management and site supervision processes. The research utilizes a large, openly available, open access audio dataset of seven different types of equipment, collected in the field under real conditions in urban regeneration projects initiated after a major earthquake in Elazığ. The dataset consists of 15,588 sounds recorded from vehicles such as bulldozers, excavators, dump trucks, graders, loaders, mixer trucks and rollers used on the construction site. In the developed classification system, discriminative features were first extracted from equipment sounds using Local Binary Patterns (LBP) and statistical moments. In the feature selection stage, the Neighborhood Component Analysis (NCA) and Chi-Square (Chi2) method is applied to identify the most significant features and dimensionality reduction is achieved. In the final stage, Support Vector Machines (SVM) and k-Nearest Neighbor (kNN) classifiers are used to discriminate the equipment types with high accuracy. The findings show that the proposed method makes a significant contribution to construction management objectives such as effective monitoring of vehicles and equipment on the construction site, resource management and process tracking. In addition, the transparency and reproducibility provided by the open dataset provides a strong basis for further studies in the related field.

Система интеллектуального контроля техники безопасности на строительных объектах

This article discusses current approaches to the use of computer vision technologies in the tasks of ensuring safety at construction sites. Special attention is paid to methods of detecting personal protective equipment, neural network architectures used for visual data analysis, as well as practical aspects of integrating such solutions into real production processes. An automated control system based on the YOLOv12 artificial intelligence model architecture has been developed. The system is designed to analyze the video stream in real time in order to identify violations related to the lack of personal protective equipment, in particular construction helmets. There are three main modules implemented in the system. Video data processing modules, detection and classification of violations, and automatic report generation. The training of the model is based on real video data, which ensures adaptation to the conditions of the construction site with variable lighting, overlapping objects and background noise. The limitations of the algorithms are related to low visibility areas and partial overlap of objects. In the future, it is planned to expand the functionality of the system for monitoring other types of personal protective equipment and integration with construction management information systems.

The Influence of Result-Based Monitoring on the Performance of Housing Projects in Nairobi

Purpose: The purpose of the article is to establish the influence of result-based monitoring on the performance of housing projects in Nairobi. Projects, including construction, aim to meet diverse stakeholder needs and are evaluated based on quality, time, and cost. The Project Management Institute (PMI) asserts that successful projects must be completed within budget, on schedule, and to the desired quality. Globally, many struggle to meet these benchmarks. Benchmarking evaluation approaches significantly influence housing project performance, yet their absence often leads to inefficiencies, cost overruns, delays, and quality issues. This study assessed the impact of benchmarking on housing projects in Nairobi, guided by Construction Management Theory. Methodology: A descriptive research design was used, targeting 127 projects by 15 real estate developers between 2019–2024. Data were collected from 254 registered architects using structured questionnaires and analyzed using SPSS to generate descriptive and inferential statistics. Findings: The findings underscore the widespread adoption and favorable perception of Result-Based Monitoring (RBM) as a project management approach in housing developments. The majority of respondents affirmed the use of RBM practices. The empirical evidence from the study aligns with international and regional findings on the positive relationship between RBM and housing project performance. While high levels of adoption and satisfaction prevail, the responses also highlight areas where targeted improvements in implementation fidelity, tool functionality, and feedback application could further enhance the impact of RBM systems. Unique Contribution to Theory, Policy and Practice: The study recommends the institutionalization of standardized RBM protocols across all housing projects. To address technical challenges, it is also recommended that organizations invest in modern monitoring tools and digital systems capable of real-time data tracking, analytics, and feedback generation. The unique contribution of the study is that Feedback loops should be institutionalized, with clear mechanisms for translating monitoring insights into actionable strategic decisions.

Influence of Benchmarking Evaluation Approaches on the Performance of Housing Projects in Nairobi

Purpose: The study purpose is to determine the influence of benchmarking evaluation approaches on the performance of housing projects in Nairobi. Projects, including construction, aim to meet diverse stakeholder needs and are evaluated based on quality, time, and cost. The Project Management Institute (PMI) asserts that successful projects must be completed within budget, on schedule, and to the desired quality. Globally, many struggle to meet these benchmarks. Benchmarking evaluation approaches significantly influence housing project performance, yet their absence often leads to inefficiencies, cost overruns, delays, and quality issues. This study assessed the impact of benchmarking on housing projects in Nairobi, guided by Construction Management Theory. Methodology: A descriptive research design was used, targeting 127 projects by 15 real estate developers between 2019–2024. Data were collected from 254 registered architects using structured questionnaires and analyzed using SPSS to generate descriptive and inferential statistics. Findings: Findings indicate strong organizational commitment to benchmarking, which positively influences project performance, decision-making, and strategic planning. However, barriers such as limited training, system integration issues, and weak policy influence hinder full adoption. Unique Contribution to Theory, Policy and Practice: The study recommends the alignment of bench marking evaluation approaches with Kenya’s affordable housing program and international standards (e.g., UN-Habitat guidelines).

Confrontation of Geniuses: The Correspondence of B. C. Rastrelli and J.-B. A. Le Blond with A. D. Menshikov in the Context of Creating Architectural Symbols of the Empire

This article presents selected documents from the correspondence of architects Carlo Bartolomeo Rastrelli and Jean-Baptiste Alexandre Le Blond with A. D. Menshikov in 1716–1717. The letters clearly demonstrate the nature of the complex relationship between the two outstanding specialists, found in a situation of fierce competition for the leading role in the design and construction management of the royal residences and St Petersburg as architectural symbols of Peter the Great’s empire. The proposed documents show the details of the conflict that took place between the architects in 1716–1717 against the background of professional disagreements and personal claims. The main arbiter in resolving controversial situations was Prince A. D. Menshikov. It was to him that Rastrelli and Le Blond sent letters outlining the essence of their argument (which resulted in a fight), seeking the support of their influential patron. The text of their correspondence testifies to a whole complex of reasons that caused the confrontation between the two geniuses. The origins of the conflict are found in the insufficiently explained schemes of the professional hierarchy and the content of the work which foreigners were hired for (as evidenced by the appeal to contracts). The contradictions led to a halt in the activities of masters and apprentices subordinate to the architects, uncertainty in the hierarchy of office and the priority power of the orders of Rastrelli and Le Blond. The personal qualities of the architects also played a role: each of them was distinguished by a difficult character, directness, stubbornness, and irrepressible ambitions. The relationship between Rastrelli and Le Blond had a direct impact on the progress of construction and the architectural appearance of the objects. The originals of letters in French presented in the collection demonstrate a high level of translation activity in the Menshikov office, which testifies to the quality and accuracy of information transmission during communications between foreign specialists and Alexander Danilovich as a spokesman for the positions of power. The published documents expand and significantly complement the ideas about the internal mechanisms of the construction of imperial space and the symbolic policy of Peter I.

Research on building cost management supported by BIM technology based on multi-intelligent body optimization algorithm

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.

Minimising Risks of Wastage in Modular Construction

Rapid growth of the construction industry has resulted an increase amount of waste generation. Modular construction on Industrialised Building System (IBS) has emerged as a significant innovation in the construction industry, offering numerous benefits such as reduced construction time, improved quality control, and enhanced sustainability. Despite these advantages, the industry still faces challenges related to material wastage, which not only impacts the environment but also contributes to cost overruns. Minimising the risk of wastage in modular construction requires a strategic approach encompassing efficient design, meticulous planning, and the adoption of advanced technologies. This study aims to comprehensively evaluate and compare strategies to minimise wastage in modular construction by achieving the following objectives: identifying types of wastage, examining factors that contribute to wastage, and proposing methods to reduce wastage effectively. This research aims to identify synergies and opportunities for integration among these approaches to optimise material usage, reduce waste, and improve overall efficiency in modular construction in Malaysia. Preliminary interviews were carried out with 5 participants in Klang Valley, focusing on Petaling district to gather data for this pilot study. In addition, a thorough literature review was also performed to identify the common waste in modular construction. The findings highlight the types of waste, influencing factors and mitigation plans to minimise the risk of wastage in modular construction. Adopting AI, BIM and IoT in modular construction projects in Malaysia offers significant potential to minimise waste by enhancing design precision, enabling real-time process monitoring, and predicting inefficiencies before they occur. Thus, the implications and potential impact of this research on the construction industry lie in identifying issues related to wastage in IBS and encouraging construction management to address waste concerns, particularly those arising from component defects in building projects.

Adoption of virtual and augmented reality in the architecture, engineering, construction, and facilities management (AEC-FM): mixed method analysis of trends, gaps, and solutions

The Architecture, Engineering, Construction, and Facilities Management (AEC-FM) industry is undergoing a transformative phase with the integration of Augmented Reality (AR) and Virtual Reality (VR) technologies. This study investigates adoption trends, challenges, and future potential of AR/VR technologies within the AEC-FM using a mixed-methods approach, including surveys of over 200 industry experts (2018, 2020, and 2023) and thematic analysis of qualitative interviews. Findings reveal initial optimism in 2018 due to technological advancements, followed by tempered expectations as limitations, costs, and implementation challenges became apparent. While adoption has been led by commercial and institutional sectors, recent growth in the industrial sector reflects AR/VR’s value in large-scale, complex projects. Furthermore, perceptions about AR/VR’s future are consistent across age and gender. The study also highlights the industry’s growing preference for outsourcing AR/VR-related tasks and the shift toward cost-effective solutions like Virtual Design and Construction (VDC) technologies reducing the need for internal AR/VR expertise.

Combining giant pacific octopus optimizer and multi-objective optimization for the multi-mode resource-constrained multi-project scheduling problem

PurposeThis research aims to introduce an algorithm, the Multi-objective Giant Pacific Octopus Optimizer (MOGPOO) and demonstrate its use in balancing three parameters: time, cost, quality in the Multi-Mode Resource-constrained Multi-Project Scheduling Problem (MRCMPSP).Design/methodology/approachThe aim of the project is to combine the Multi-objective Optimization with a Giant Pacific Octopus Optimizer (GPOO) – swarm intelligence approach. The study compared the derivatives of the MOGPOO optimization method to those of other algorithms, such as the Multi-objective Slime Mold Algorithm (MOSMA) and the Multi-objective Grey Wolf Optimization (MOGWO), and testing the model’s performance and assessing the construction problem, a total of nine projects in the essential MRCMPSP problem are taken into consideration from the resources that are accessible.FindingsCompared to the other approaches, MOGPOO performed better on the majority of the assessment criteria. This study and its results can be useful for researchers working on multi-project scheduling models. In addition, the superiority of MOGPOO becomes increasingly evident as the complexity of the problem increases.Originality/valueA hybrid swarm intelligence model and strategy that allows Multi-Mode Resource-constrained Multi-Project Scheduling Problem in construction management is presented in the study in order to optimize the ideal solution in the search space. Based on the findings of this study’s testing procedure, a strong model that outperformed the examined models was constructed.

Implementation of Integrated Smart Construction Monitoring System Based on Point Cloud Data and IoT Technique

This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously.

Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications

Background: The rapid integration of generative AIs, such as ChatGPT, into industrial, process, and construction management introduces both operational advantages and emerging cognitive risks. While these models support task automation and safety analysis, their internal architecture differs fundamentally from human cognition, posing interpretability and trust challenges in high-risk contexts. Methods: This study investigates whether architectural design elements in Transformer-based generative models contribute to a measurable divergence from human reasoning. A methodological framework is developed to examine core AI mechanisms—vectorization, positional encoding, attention scoring, and optimization functions—focusing on how these introduce quantifiable “distances” from human semantic understanding. Results: Through theoretical analysis and a case study involving fall prevention advice in construction, six types of architectural distances are identified and evaluated using cosine similarity and attention mapping. The results reveal misalignments in focus, semantics, and response stability, which may hinder effective human–AI collaboration in safety-critical decisions. Conclusions: These findings suggest that such distances represent not only algorithmic abstraction but also potential safety risks when generative AI is deployed in practice. The study advocates for the development of AI architectures that better reflect human cognitive structures to reduce these risks and improve reliability in safety applications.

Artificial intelligence in civil engineering: emerging applications and opportunities

Artificial intelligence (AI) is rapidly transforming civil engineering by harnessing vast data streams and advanced computational methods. This review provides a comprehensive survey of AI innovations in civil engineering, covering key technologies (machine learning, deep learning, natural language processing, computer vision, robotics, and generative AI) and their applications across design, construction, monitoring, transportation, geotechnical, environmental, and asset management domains. This paper discuss how AI-driven models and systems improve efficiency, safety, and sustainability, while also addressing challenges such as data limitations, model interpretability, and ethical concerns. Emerging trends—such as digital twins, smart cities, and quantum computing—are highlighted, along with the growing need for workforce skills in AI. By synthesizing recent studies, this article aims to clarify how AI is reshaping civil engineering practice and to identify opportunities and gaps for future research.

Metaheuristic Optimization for Efficient Construction Management of Building Projects

Effective management of production systems in the construction industry requires a complex solution of organizational and technical problems related to the coordination of heterogeneous optimization criteria performance. The primary impediment to real estate development is the complexity of making informed management decisions. Conflicting interests among various stakeholders can lead to disruptions in production processes and potential crisis situations. The aim of article is to explore the potential for adapting metaheuristic optimization methods on step of earthworks enhance intelligent construction management technologies. This will result in improving decision-making processes, optimize production workflows, increase productivity, and achieve strategic objectives. This research is inspired by the mechanisms of biological systems in nature. The innovation of the study described in the article lies in the development of an automated control algorithm based on metaheuristics, which broadens the application of digital information technologies across the stages of design, modeling, implementation of construction projects, and quality management of building operations. The result of the research allows us to optimize worker productivity during building construction. The high reliability of the models obtained clearly shows that metaheuristics can successfully improve construction processes and digital transformation can be effectively implemented.