AI-Based Predictive Maintenance System for Construction

Abstract

This project focuses on the development of a software-based system for predicting structural cracks in buildings using machine learning techniques. In the construction industry, structural cracks are a common problem that can lead to serious safety issues, increased maintenance costs, and reduced durability of buildings. Traditional inspection methods rely heavily on manual observation and periodic checking, which are time-consuming and may fail to identify early-stage defects. Therefore, there is a need for an intelligent and efficient system that can predict such issues in advance.The proposed system utilizes various construction-related input parameters such as cement thickness, temperature conditions, material quality, load applied on the structure, and age of the building. These parameters are provided as input to the system either manually by the user or through available datasets. The collected data is then processed and analyzed using machine learning algorithms to identify patterns and relationships associated with structural cracks.The system is designed to train predictive models using historical construction data, enabling it to classify whether a building is likely to develop cracks or not. By applying suitable algorithms such as Decision Tree, Random Forest, or Logistic Regression, the system improves prediction accuracy and reliability. The output is presented in a simple and user-friendly format, allowing engineers and construction professionals to make informed decisions.This software-based approach provides early warning about potential structural issues, helping to take preventive measures before severe damage occurs. As a result, it significantly enhances building safety, reduces repair and maintenance costs, and improves the overall quality and lifespan of construction projects. Furthermore, the system can be extended in the future by incorporating additional parameters and advanced algorithms to achieve better performance and wider applicability.