Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future
Journal of Economics, Management and Trade · pp. 75–87 · Published 14 May 2024
10.9734/jemt/2024/v30i61215Abstract
The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.
References (16)
- 1 Big Data in the construction industry: A review of present status, opportunities, and future trends [DOI]
- 2 Computer vision techniques for construction safety and health monitoring [DOI]
- 3 Cloud computing in construction industry: Use cases, benefits and challenges [DOI]
- 4 Safety leading indicators for construction sites: A machine learning approach [DOI]
- 5 Digital Twins for the built environment: learning from conceptual and process models in manufacturing [DOI]
- 6 From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Artificial Intelligence and Further On [DOI]
- 7 A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies [DOI]
- 8 Integrating three-dimensional road design and pavement structure analysis based on BIM [DOI]
- 9 Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies [DOI]
- 10 Survey of digital technologies in procurement of construction projects [DOI]
- 11 Deep learning with small datasets: using autoencoders to address limited datasets in construction management [DOI]
- 12 BIM competencies for delivering waste-efficient building projects in a circular economy [DOI]
- 13 Challenges Involved in Adopting BIM on the Construction Jobsite [DOI]
- 14 Investigating antecedents and drivers affecting the adoption of collaboration technologies in the construction industry [DOI]
- 15 Smart optimization for mega construction projects using artificial intelligence [DOI]
- 16 Role of Artificial Intelligence in the Construction Industry – A Systematic Review [DOI]
Cited by 0
No indexed citations yet.
Related research
- Prevalence of Falls and Associated Factors among Construction Workers in Port Harcourt, Nigeria — shares topic coverage
- Evaluation of Workplace Safety Culture Implementation and Practice Using Agglomerative Hierarchy Clustering — shares topic coverage
- Factors Affecting Maintenance of Buildings in Petroleum Training Institute, Uwvie and Delta State University Abraka, Delta State, Nigeria — shares topic coverage
- Adaptability of COVID-19 Safety Guidelines in Building Construction Sites in Anambra State, Nigeria — shares topic coverage
- Evaluation of Health, Safety & Environment Culture Perception by Construction Workers in Owerri Metropolis, Nigeria — shares topic coverage
Article metrics
Real usage data collected on this platform.
5
Page views
0
PDF downloads
3
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.