AI-Enhanced Decision Support Systems for Optimizing Hazardous Waste Handling in Civil Engineering

Abstract

The planning, monitoring, and mitigating hazardous waste in civil engineering projects is complex and vital to safeguard the environment and public health. Recently, AI has become a strong tool for optimizing dangerous waste treatment. This paper examines how AI is used in civil engineering Decision Support Systems to improve hazardous waste management’s efficiency, safety, and sustainability. Hazardous waste in civil engineering presents issues and requires innovative solutions. It then discusses how machine learning algorithms, data analytics, and predictive modelling optimize trash collection, transportation, treatment, and disposal. These AI-enhanced technologies improve risk assessment and environmental compliance by monitoring and making real-time decisions. This study examines case studies and projects of AI-based Decision Support Systems to determine their pros and cons. It covers AI’s ethical and regulatory implications in hazardous waste management. AI-enhanced Decision Support Systems may optimize hazardous waste handling in civil engineering, reducing environmental impact, improving safety, and increasing productivity. This research shows that AI might revolutionize dangerous waste management in civil engineering projects and encourage sustainable, environmentally friendly solutions.