Artificial intelligence models to predict optimal trade-off on construction management

Abstract

Purpose

This research aims to introduce a novel algorithm, the Chaotic Giant Pacific Octopus Optimizer (CGPOO) and demonstrate framework includes four key aspects: time, cost, quality and safety trade-off (TCQST).

Design/methodology/approach

Artificial intelligence is causing a big disruption in the construction management. It is being used to building projects to enhance efficiency, safety and decision-making. This research compared the CGPOO method to those of other algorithms, such as the Chaotic Slime Mold Algorithm (CSMA), the Chaotic Salps Swarm Algorithm (CSSA) and the Chaotic Whale Optimization Algorithm (CWOA) and assessed the efficacy of the method using statistical analysis and evaluation indicators such as Hyper-volumn (HV), Spread (Sp), Computational Time (CT) and C-metric.

Findings

The analysis demonstrates that using CGPOO outperforms standalone methods chosen from the literature in terms of outcomes. It is discovered that the CGPOO solution possibilities for each factors are more efficient and beneficial than the comparison algorithms. Moreover, the CGPOO model performs better than the other algorithms with quality indices C-metric, Sp, HV and CT of 0.534, 0.531, 0.891 and 101.

Originality/value

The article presents a novel hybrid CGPOO that permits multi-factor trade-offs in construction management with the goal of surpassing the analyzed models and optimizing the optimal solution in the search space.

Citation

Pham, V.H.S. and Ngoc Quynh Khoi, L. (2024), "Artificial intelligence models to predict optimal trade-off on construction management", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-06-2024-0698