AI-Powered DPR Quality Assessment and Risk Prediction System

Abstract

The evaluation of Detailed Project Reports (DPRs) in government infrastructure projects is traditionally a manual and time-consuming process, often leading to inconsistencies, delays, and cost overruns. This paper proposes an AI-Powered DPR Quality Assessment and Risk Prediction System that automates the analysis of DPR documents using Natural Language Processing (NLP) and Machine Learning techniques. The system extracts key information such as project objectives, budget, and timelines, evaluates document quality based on predefined criteria, and predicts risks such as cost overruns and delays using models like XGBoost and LightGBM. Additionally, Explainable AI techniques and Monte Carlo simulations are used to provide transparent insights and probabilistic risk analysis. The system improves efficiency, ensures standardized evaluation, and supports data-driven decision-making in government project management.

Introduction

The text describes an AI-powered system for automating the evaluation of Detailed Project Reports (DPRs) used in large infrastructure projects in India. Traditional DPR assessment is manual, slow, inconsistent, and prone to errors, often leading to project delays, cost overruns, and poor decision-making. To address this, the proposed system uses Natural Language Processing (NLP) and machine learning to automatically analyze DPR documents, extract key information, evaluate quality, and predict project risks.

The system processes uploaded DPRs through a web-based platform where documents are parsed using OCR and text extraction techniques. NLP methods identify important sections such as objectives, timelines, financial details, and technical content, converting unstructured data into structured form. A quality scoring module evaluates completeness, compliance, and technical depth, while machine learning models (XGBoost and LightGBM) predict risks like delays, cost overruns, and feasibility issues.

The architecture follows a multi-layer design with a frontend dashboard, a FastAPI backend, and a PostgreSQL database. It integrates document processing, NLP analysis, quality assessment, risk prediction, and explainable AI to provide transparent and data-driven outputs.

Conclusion

The proposed AI-Powered DPR Analysis System provides an efficient and intelligent solution for evaluating infrastructure project reports. By automating document analysis, quality assessment, compliance validation, and risk prediction, the system improves accuracy, reduces manual effort, and supports data-driven decision-making. The system helps in identifying potential risks early, ensuring better project planning and reducing cost overruns and delays. Overall, it enhances transparency, consistency, and efficiency in government project evaluation. In the future, the system can be enhanced by integrating advanced explainability techniques such as SHAP, supporting multi-language DPR analysis, and deploying the system on cloud platforms for large-scale usage. Integration with government databases and real-time project monitoring systems can further improve its effectiveness and applicability.

References

[1] AI-Based Project Risk Prediction using Machine Learning, IEEE (2023) [2] Natural Language Processing for Document Analysis, Elsevier (2022) [3] Intelligent Document Processing using OCR and NLP, IEEE Access (2023) [4] XGBoost and LightGBM for Predictive Analytics in Infrastructure Projects (2024) [5] Explainable AI for Decision Support Systems, IEEE (2023)

Copyright

Copyright © 2026 Medishetty Swapna, Kasarla Narasimha Reddy, Lagudu Ramkishore, Panthini Sathvik . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.