Work

Optimising concreting equipment operations in India: An artificial intelligence and reliability-based approach

Year: 2025

Type: article

Abstract: This study applies reliability engineering principles and robust statistical modelling techniques to predict repair time and failure rates for concreting equipment. Data was sourced from 15 distinct construction sites, encompassing an analysis of 52 separate pieces of concreting equipment. Critical parameters impacting construction equipment productivity were identified and quantified, and subsequently formulated into a comprehensive mathematical equation. User-friendly mathematical equations were derived, capable of predicting both the mean time to repair parameter and subsequent equipment failure, following thorough data modelling procedures. A highly accurate, generalised model was created that predicts failure rates for three distinct types of concreting equipment: concrete batching plants, transit mixers , and concrete pumps, thus enhancing the practical applicability and relevance of the study within the construction industry. These findings offer valuable insights for construction professionals, allowing them to enhance equipment planning, resource allocation , and preemptive maintenance strategies. This research contributes to bridging the gap in understanding concreting equipment reliability and introduces a predictive model tailored to distinct equipment types.

Source: Expert Systems with Applications

DOI: 10.1016/j.eswa.2025.126672

Authors: Arka Ghosh, Jemal Abawajy

Institution: Deakin University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,500