New Zealand Building Project Cost and Its Influential Factors: A Structural Equation Modelling Approach

Construction industry significantly contributes to New Zealand's economic development. However, the delivery of construction projects is usually plagued by cost overruns, which turn potentially successful projects into money-losing ventures, resulting in various other unexpected negative impacts. The objectives of the study were to identify, classify, and assess the impacts of the factors affecting project cost in New Zealand. The proposed research model was examined with structural equation modelling. Recognising the lack of a systematic approach for assessing the influencing factors associated with project cost, this study identified 30 influencing factors from various sources and quantified their relative impacts. The research data were gathered through a questionnaire survey circulated across New Zealand construction industry. A total of 283 responses were received, with a 37% response rate. A model was developed for testing the relationship between project cost and the influential factors. The proposed research model was examined with structural equation modelling (SEM). According to the results of the analysis, market and industry conditions factor has the most significant effect on project cost, while regulatory regime is the second-most significant influencing factor, followed by key stakeholders' perspectives. The findings can improve project cost performance through the identification and evaluation of the cost-influencing factors. The results of such analysis enable industry professionals to better understand cost-related risks in the complex environment

Installation Quality Inspection for High Formwork Using Terrestrial Laser Scanning Technology

Current inspection for installation quality of high formwork is conducted by site managers based on personal experience and intuition. This non-systematic inspection is laborious and it is difficult to provide accurate dimension measurements for high formwork. The study proposed a method that uses terrestrial laser scanning (TLS) technology to collect the full range measurements of a high formwork and develop a genetic algorithm (GA) optimized artificial neutral network (ANN) model to improve measurement accuracy. First, a small-scale high formwork model set was established in the lab for scanning. Then, the collected multi-scan data were registered in a common reference system, and RGB value and symmetry of the structure were used to extract poles and tubes of the model set, removing all irrelevant data. Third, all the cross points of poles and tubes were generated. Next, the model set positioned on the moving equipment was scanned at different specified locations in order to collect sufficient data to develop an GA-ANN model that can generate accurate estimates of the point coordinates so that the accuracy of the dimension measurements can be achieved at the millimetre level. Validation experiments were conducted both on another model set and a real high formwork. The successful applications suggest that the proposed method is superior to other common techniques for obtaining the required data necessary for accurately measuring the overall structure dimensions, regarding data accuracy, cost and time. The study proposed an effective method for installation quality inspection for high formwork, especially when the inspection cannot be properly operated due to cost factors associated with common inspection methods

BIM Adoption in the Cambodian Construction Industry: Key Drivers and Barriers

Critical issues surrounding the promotion and adoption of building information modeling (BIM) for construction projects are largely country-specific due to contextual socio-cultural, economic, and regulatory environments impacting construction operations and outcomes. There is little information on BIM adoption issues specific to the Cambodian construction industry (‘the industry’). This paper aims to narrow existing knowledge by investigating key drivers for, and barriers to the adoption of BIM in the industry. Using descriptive survey method, feedback was received from contractors and architects that were registered with their respective trade and professional associations in the industry. The multi-attribute method and the Statistical Package for the Social Sciences (SPSS)-based Kendall’s coefficient of concordance (W) test were used to analyze the empirical datasets. Results showed that out of the 13 significant drivers identified in the study, the most influential comprised the technology’s ability to remarkably enhance project visualization and schedule performance; this is followed by awareness that the technology is redefining how project information is created and shared among stakeholders and therefore the future of the industry that cannot be ignored. On the other hand, the most constraining barrier to the adoption of the technology, out of 19 significant barriers, related to strong industry resistance to change, especially reluctance to change from 2D drafting to 3D modeling; other highly rated barriers included the high initial cost of the software and the shortage of professionals with BIM skills. Implementation of the study findings could support greater uptake of the technology and the leveraging of its key benefits to improving project success and the growth of the Cambodian construction industry, as well as those of other developing economies that share similar socio-cultural, economic, and regulatory environments

Factors influencing adoption of construction technologies in Vietnam's residential construction projects

PurposeVietnam's construction technology (CT) adoption is low when compared to other countries with similar gross domestic product (GDP) per capita resulting in lesser productivity. The research objectives are: (1) To undertake an extensive literature review on CT adoption challenges; (2) To investigate CT adoption challenges unique to Vietnam's construction sector; and (3) To propose data-driven solutions for a greater rate of CT adoption.Design/methodology/approachA two-stage descriptive survey method was adopted in alignment with the research aim and objectives. Based on the literature review of 215 articles, a questionnaire was designed and administered to experienced construction managers (CM) to identify whether CT has been adopted, barriers to adoption, drivers, and the most popular CT tools. Descriptive statistics were used to summarize the characteristics of interest in the empirical dataset and SPSS-based inferential statistics to estimate the means, frequency counts, variance and test hypotheses that informed the drawing of conclusions concerning the research objectives.FindingsThe popular CT tools identified were Autodesk, Microsoft Office and Primavera. The most influential CT adoption barriers: (1) Unknow`n impact on productivity, (2) Late implementation of software in construction projects, (3) Lack of understanding of importance and needs in the construction industry (4) Lack of funds during budget planning for technological advances and implementation (5) Lack of experts required for technological change, and insufficient skills in the industry.Practical implicationsIt is expected that the findings could inform data-driven regulatory and practice reforms targeted at increasing greater uptake of CT in Vietnam with potential for replication in countries facing similar adoption challenges.Originality/valueThe findings are intended to support data-driven regulatory and practice improvements aimed at improving CT adoption in Vietnam, with the possibility for replication in other countries facing comparable problems.</jats:sec

Multi-Sensor Data Fusion for 3D Reconstruction of Complex Structures: A Case Study on a Real High Formwork Project

As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite

Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review

The construction industry faces risks across various domains, including cost, safety, schedule, quality, and supply chain management. Recent artificial intelligence (AI) advancements offer promising solutions to enhance risk management. This systematic literature review (SLR) explores the integration of AI in construction risk management, focusing on AI applications, risk categories, and key algorithms. A total of 84 peer-reviewed articles published between 2014 and 2024 were analysed. The SLR method involved rigorous identification, selection, and critical appraisal of studies, followed by bibliometric analysis to uncover research trends, influential authors, and thematic clusters. The bibliometric analysis, including keyword co-occurrence and author collaboration networks, provided insights into the structure of the research landscape. Findings revealed that AI methods such as machine learning (ML), natural language processing (NLP), knowledge-based reasoning (KBR), optimisation algorithm (OA), and computer vision (CV) play crucial roles in predicting and managing risks. ML is employed for predictive modelling, NLP for document and compliance risk management, KBR for decision support, OA for optimising resources and schedules, and CV for real-time safety monitoring. Despite advancements, challenges related to data quality, model interpretability, and workforce skills hinder full AI integration. Future research should explore AI’s intersection with emerging technologies such as blockchain and adaptive risk models for responsible adoption. This paper contributes to the growing knowledge of AI’s transformative impact on construction risk management