Predictive Construction Equipment Maintenance Using Neural Networks
Publication: Construction Research Congress 2024
ABSTRACT
Construction equipment is subject to several types of breakdown throughout the project duration. The availability of operational data is crucial to understand the breakdown patterns and implement effective maintenance strategies. However, projects often operate with tight profit margins and limited resources; and therefore, access to such data is not readily available. The aim of this study is to establish a predictive maintenance framework based on machine learning (ML) that leverages historical breakdown data with the absence of information relating to the condition of the equipment and any output extracted from monitoring devices and sensors. The proposed approach entails developing and applying a multilayer perceptron (MLP) neural network to a real-life infrastructure project in the Middle East. The collected data include an equipment maintenance log database. The results show a significant improvement in accuracy compared to linear and non-linear models reported in the literature. The proposed framework helps enhancing the overall productivity of construction equipment by minimizing their breakdown rate, thereby reducing the associated operating costs.
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Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 50 - 59
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Building management
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction management
- Data collection
- Engineering fundamentals
- Equipment and machinery
- Financial management
- Maintenance and operation
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Practice and Profession
- Profits
- Project management
- Research methods (by type)
Authors
Metrics & Citations
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