Authors:
S. Rubin Bose, J. Angelin Jeba, R. Vinoth, R. Regin, K. Senthamilselvan, Madhan Raj Gopi Akila

Addresses:
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. Department of Electronics and Instrumentation Engineering, Madras Institute of Technology, Chennai, Tamil Nadu, India. School of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science, University of Stuttgart, Stuttgart, Baden-Württemberg, Germany.

Abstract:

The purpose of this study is to overcome the constraints of traditional manual bridge inspection methods by presenting the creation of a Bridge Condition Analysis and Prediction System that makes use of ANNs. The method uses a data-driven approach, training specialised artificial neural network models on large bridge datasets to accurately predict five crucial structural health and maintenance parameters. The system generates actionable reports that include prioritised repair plans, risk assessments, and lifecycle analyses. These reports enable engineers and decision-makers to optimise resource allocation and adopt proactive maintenance strategies. The system accomplishes this by integrating predictive modelling with an intelligent analysis engine. Scalability and extensibility are ensured by the modular architecture, which can accommodate a wide range of infrastructure applications. Interactive web-based dashboards are also being developed to visualise condition trends and to simulate maintenance dynamically. In conclusion, this system is a significant improvement in artificial intelligence-driven public infrastructure management. It combines data-driven learning, interpretive analytics, and decision-support intelligence to improve the safety of bridges, their durability, and their cost-effectiveness. This establishes a solid foundation for future advances in intelligent civil engineering and smart infrastructure governance. 

Keywords: Public Assault; Human Activity Recognition; Artificial Intelligence; Predictive Model; Machine Learning; Behavioural Analysis; Bridge Condition Analysis; Prediction System; Artificial Neural Networks (ANNs).

Received on: 15/02/2025, Revised on: 22/04/2025, Accepted on: 17/07/2025, Published on: 03/03/2026

DOI: 10.69888/FTSESS.2026.000691

FMDB Transactions on Sustainable Environmental Sciences, 2026 Vol. 3 No. 1, Pages: 20-32

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