Artificial Intelligence Based Structural Inspection, Damage Detection And Remaining Life Prediction

Abstract

Civil structures such as buildings, roads, railways, bridges, tunnels, and dams are present in every society, and the safest and most durable ones are those that are properly managed and maintained. Health monitoring plays an important role in ensuring safety, but engineers still depend on traditional inspection tools like crack gauges or comparator cards, which are slow, labour-intensive, and differ based on the inspector's judgment. There is also no proper tool that systematically analyses this data or suggests solutions based on standard codes using AI. Considering these limitations, this project aims to develop an AI model that processes images to classify whether a defect is structural or non-structural, identify the type of defect, assign a severity score where 0 is worst and 100 is best, suggest causes and remedies using an LLM, and estimate the remaining service life. Literature supports the need for such a system, as studies have focused on deep-learning-based detection of concrete damage, CNN-based comparison between traditional and AI inspection, and 3D scanning with machine learning for defect detection. Surveys on NDT tools like UPV and X-ray show improved efficiency over manual methods but still remain time-consuming and less precise. Most research concentrates on a single defect such as cracks, and although some models can detect or localize them, there is still a major gap in systems that quantify severity or provide causes and repair suggestions. Many approaches stop at detection without offering guidance for maintenance, making it difficult to translate inspection data into decisions. To address this, datasets in this project are collected from public sources and field surveys, followed by preprocessing such as histogram equalization and median filtering. Images are then manually labelled into structural and non-structural categories and further classified by defect type. An image classification model is trained on this dataset to automatically detect and classify defects. Severity scoring is done using the formula Intensity × Extent, where intensity depends on measurable parameters like crack width or spall area, and extent depends on defect length or percentage affected. The model uses an LLM aligned with Indian Standard codes to suggest possible causes and remedies and predicts remaining service life based on severity. The novelty of this work lies in combining multiple functions that previous studies treat separately: multi-defect classification, quantitative severity scoring, LLM-based engineering interpretation, and service-life estimation. No existing system integrates all these components into a single workflow dedicated to structural inspection. Buildings are long-term investments, and regular monitoring helps extend their lifespan and prevent failures. This project aligns with SDG 9, SDG 11, and SDG 12 by supporting safer, more resource-efficient, and sustainable infrastructure management. By bringing together defect detection, severity assessment, interpretation, and life prediction, the work addresses limitations of manual inspections and gaps in current automated tools. It supports better maintenance planning and contributes to a more comprehensive AI-based structural health-monitoring framework.