AEHO: Apriori-Based Optimized Model for Building Construction to Time-Cost Tradeoff Modeling

Abstract

The operations involved in building a structure interact and depend on one another. The model is developed using an Apriori-based swarm intelligence method, with the non-dominated solutions to the separation of elephant herding optimization technique as the swarm intelligence method. Also, population initialization, selection, and fitness evaluation for input parameters are used. This strategy optimizes construction time, cost, & environmental effects in an actual construction project. Compared to the original simulation-optimization integration, the suggested machine learning-based solution dramatically reduces computing time. A case study of a building construction project has been employed to show the usability of the proposed method. Similarly, the usefulness of the proposed AEHO model in optimal design is demonstrated by measuring many performance measures and a comparison with an already existing PSO optimization model. In addition, a coefficient value plot is established for visualizing the provided objectives, and an apriori method is presented for selecting one solution from the Pareto-optimal front that has been generated. This study intends to minimize time and cost for construction projects that include repetitive project activities by using the learning curve phenomenon, which reduces time and cost savings when considering the project's start and finish dates.

Optimization of Tunnel Construction Schedule Considering Soft Logic

“…Ze et al employed the gray wolf optimization algorithm to provide insights into the issue [26]. In the practical construction process, heuristic methods have been extensively applied to the scheduling optimization of repetitive construction projects, in which the application of GAs is particularly significant [27][28][29].…”