Assessing AI's Role in Construction: A Hybrid Multi-Criteria Decision-Making Approach

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Aug 3 2023

In a paper published in the journal Sustainability, researchers investigated the utilization of artificial intelligence (AI) tools in the construction sector. They employed a hybrid multi-criteria decision-making (MCDM) approach that incorporated the Delphi method, the technique for order of preference by similarity to the ideal solution (TOPSIS), and the analytic network process (ANP) within a fuzzy context.

Background

The advent of AI technologies has brought about a substantial transformation in the construction industry. AI's capabilities in data processing, complex pattern analysis, and informed decision-making have revolutionized various aspects of construction. Embracing AI's potential responsibly can lead to improved efficiency, cost reduction, enhanced quality, and innovation in the construction sector. The present study aims to explore AI's impact on design, project planning, construction processes, and industry performance.

Significance of AI technologies in construction

AI's significance in construction cannot be overstated, as it offers numerous benefits, including improved safety and sustainability. Automation, predictive analytics, and data-driven decision-making enhance productivity and reduce project timelines. Real-time monitoring with AI algorithms improves safety and risk management. AI-based cost estimation and resource allocation optimize budgets and reduce expenses.

AI-powered design tools and virtual reality aid in better planning and collaboration. Furthermore, AI fosters innovation by integrating with the building information model (BIM) and analyzing historical data. Sustainable practices are promoted through AI-driven optimization of energy usage and material selection, unlocking higher performance, competitiveness, and sustainability in the construction industry.

Literature review

The integration of artificial intelligence (AI) technologies is transforming the construction industry. The authors conducted a literature review providing an overview of key studies on AI in construction, highlighting applications, benefits, and challenges. Brainstorming sessions with experts and database research were conducted to access relevant articles.

The review emphasizes AI's role in project planning, design, construction processes, and safety management. AI offers efficiency, safety, and innovation, though complexity can hinder adoption. Challenges include data quality, technical barriers, and ethical considerations. However, AI presents opportunities for process optimization, enhanced decision-making, and safety improvements.

Framework and methodology

The theoretical framework presents a systematic approach through MCDM strategies to achieve specific objectives. The methodology encompasses forming an expert panel, identifying crucial parameters, verifying selected parameters, performing ANP analysis, and applying the TOPSIS MCDM technique to rank AI alternatives. The hybrid approach ensures comprehensive evaluation and consideration of local factors.

The Delphi method is used initially, followed by ANP and TOPSIS, to examine the suitability of the 13 selected parameters. A panel of 30 experts, including 5 from India and 5 from China, independently provided qualitative judgments in 10 rounds, with their expressions transformed into triangular fuzzy numbers for analysis.

The ANP involves pairwise comparisons to determine the relative importance of criteria, considering direct and indirect influences. The experts' judgments were validated for consistency, and final criteria weights were obtained. In TOPSIS analysis, a decision matrix was created, normalized, and weighted. Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) were determined, and the relative closeness coefficient (RCC) was calculated to rank alternatives. These systematic methods allow comprehensive evaluation and ranking, providing valuable insights for complex decision-making in various domains, including strategic planning, resource allocation, and risk assessment.

Results

The ANP analysis revealed technological hurdles as the most crucial factor (67.9%), followed by environmental factors (24.4%) and organizational criteria (7.7%). Data and algorithms, innovations for traditional problems, and trust between companies are the most significant sub-factors. TOPSIS analysis ranks the second alternative as the best option, aligning with the ANP results. These findings provide valuable insights for decision-makers and enhance understanding of AI's impact on the construction industry.

Strengths and weaknesses of the proposed model

The Delphi-ANP-TOPSIS hybrid approach has its benefits and limitations. The Delphi method gathers expert feedback for unbiased criteria weighting, while the ANP captures criteria interdependencies, and TOPSIS ranks alternatives based on ideal solutions. The fuzzy environment addresses uncertainties and imprecise data. Thus, the strengths of this model include consensus-building, comprehensive decision-making, and systematic evaluation. However, it can be time-consuming, and subjectivity may influence results, requiring expertise in each method. Despite the limitations, the hybrid model's strengths make it a compelling choice for analyzing AI's role in construction.

Conclusion

In summary, the authors proposed a novel approach, integrating the Delphi method, ANP, and TOPSIS as a hybrid MCDM concept to assess AI's role in construction. This comprehensive evaluation addresses uncertainty and fills research gaps. It informs decision-makers and industry professionals about implementing AI effectively. Further research can advance AI technologies in construction and improve decision-making. Parameters and AI alternatives are crucial for informed judgments in assessing AI's impact on the construction industry.