An intelligent simulation-based framework for automated planning of concrete construction works

Abstract

Purpose

This paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.

Design/methodology/approach

In this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.

Findings

The authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.

Originality/value

The active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.

Citation

Mohammadi, S., Tavakolan, M. and Zahraie, B. (2022), "An intelligent simulation-based framework for automated planning of concrete construction works", Engineering, Construction and Architectural Management, Vol. 29 No. 2, pp. 916-939. https://doi.org/10.1108/ECAM-11-2020-0971