Abstract
Construction projects are influenced by various uncertain factors, including supervisory knowledge, labor expertise, and weather conditions. These uncertain factors have linguistic properties that are challenging to quantify with the help of traditional set theory. To address this, the study applies fuzzy inference systems, specifically the Mamdani fuzzy inference system, to model and evaluate the impact of these uncertainties on construction projects. This fuzzy inference system provides a more nuanced understanding of these factors compared to traditional set theory methods. Experimental results indicate that uncertainties significantly impact construction projects. These findings establish a valuable framework for developing adaptive strategies, enhancing project planning, and improving durability to unexpected project dynamics. Additionally, the research have integrated fuzzy logic with the recently developed Nature-inspired Metaheuristic Knowledge-based algorithm, providing an advanced approach to optimize time and cost objectives even under fluctuating project conditions. The experimental findings demonstrate its effectiveness in addressing complex, real-world multi-objective optimization problems.
Recommended Citation
Kapoor, M.; Pathak, B. K.; and Kumar, R.
(2025).
(SI15-017) Modeling Construction Project Uncertainty using Fuzzy Inference and Nature-inspired Metaheuristic Knowledge-based Algorithms,
Applications and Applied Mathematics: An International Journal (AAM), Vol. 20,
Iss.
4, Article 3.
Available at:
https://digitalcommons.pvamu.edu/aam/vol20/iss4/3