Barriers to Building Information Modeling (BIM) Deployment in Small Construction Projects: Malaysian Construction Industry

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Building information modeling (BIM) application in construction projects is considered beneficial for effective decision making throughout the project lifecycle, as it maximizes benefits without compromising practicality. The Malaysian construction industry is also keen on the adoption of BIM culture. However, various identified and unidentified barriers are hindering its practical implementation. In light of this, this study identified and analyzed critical obstacles to using BIM in Malaysian small construction projects. Through the use of semi-structured interviews and a pilot study using the exploratory factor analysis (EFA) method, the critical BIM barriers (CBBs) have been identified. Based on the findings of the EFA, CBBs were classified into five categories, i.e., technical adoption barrier, behavioral barrier, implementation barrier, management barrier, and digital education barrier. Following the questionnaire survey, feedback of 235 professionals was collected with vested interests in the Malaysian construction business, and the CBBs model was created using analysis of moment structures (AMOS). The findings revealed that although Malaysian experts with little experience in practice were fairly educated about BIM, technical adoption barriers, behavioral barriers, management barriers, and implementation hurdles were critical for adopting BIM. The study’s findings will help policymakers eliminate CBBs and use BIM in Malaysia’s modest construction projects to save costs, save time, boost productivity, and improve quality and sustainability.

Related collections

Most cited references 169

Record: found
Abstract: found
Article: found

Is Open Access

State-of-the-art in artificial neural network applications: A survey

Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara … (2018)

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.