NLP Enable Infrastructure Compliant Intelligence for Automated Triage Root Cause Analytics and Response Time Optimization in Municipal Road Drainage and Utility Maintenance Systems in the United States

Abstract Municipal drainage maintenance departments are increasingly utilizing text-based documents such as work orders, inspection logs, and service reports as tools to deal with aging infrastructure; however, the predictive analytics associated with those documents is seldom used. This project provides an artificial intelligence (AI) based framework for analyzing municipal records using natural language processing (NLP) techniques and extracting information associated with the major activities performed during maintenance, the occurrence of failure indicators, and the underlying causes of the failures. This framework is focused on the use of NLP techniques in triage and explainable prioritization of data to allow for expedited response times to drainage work orders where the agencies have limited resources, limited regulatory constraints, and insufficient personnel availability, which are common limitations in municipalities across the United States. The integration of descriptive and inferential data-driven insights provided by NLP with machine learning based decision making logic will facilitate the movement away from reactive-based maintenance strategies that are scheduled as failures occur, to data-driven, compliance based intervention strategies. The primary contribution of the project is demonstrating how NLP can act as the central intelligence layer for infrastructure maintenance operations and provide scalable, transparent and operationally viable support for urban drainage systems with resilience.