Cost-Optimized Pre-Engineered Buildings: A Bim-Based Meta Analysis

Abstract

In recent times, the usage of Pre -engineered buildings (PEB), a method exists that provides benefits for building 
structures quickly and efficiently while using materials effectively and economically. However, to fully realize the 
potential of PEBs, advanced design optimization tools and stringent cost efficiency frameworks must be 
incorporated. The aim of this paper is to discuss a systematic review and meta-analysis of articles that were 
published from 2010 until 2024 focused on the implementation of Building Information Modelling (BIM) or 
specific structural software such as STAAD. In terms of a literature review, the following are traffic or software 
analysis used in different research papers: Pro, ETABS, SAP2000 and Tekla Structures and other PEB systems 
status prediction that has taken its place comfortably for an optimal use of figures based on some economic 
parameters. The evidence is drawn from 30 peer reviewed studies, and covers several dimensions such as 
structural mass reduction, material use efficiency, load distribution optimization, fab costs savings etc. It was 
discovered that design processes involving PEB integration with BIM material savings ranged from 15–30% 
lower in weight than the conventional process and optimization software for structures reduced steel consumption 
by up to a maximum of 25%. Using BIM along with structural tools has also been indicated to yield consistent 
reductions in project delivery timelines and lifecycle costs, as illustrated in the meta-analysis. Some of the key 
barriers identified are limited interoperability between software platforms, talent shortage and lack of well 
defined PEB-BIM integration frameworks in developing economies. In this paper, we present AI-Driven Design 
Optimization for an Effective parametric Metal Structure which is a better dimensional strategy to achieve 
economic viability and cost efficiency in PEB Design while ensuring minimum information loss and time in the 
design process though further incorporating effective parametric structures by integrating Ai-driven Parametric 
optimization embedded with real-time structural health monitoring systems integrated into the BIM environments 
as future directions of research.