Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles

TL;DR

This paper introduces an explainable AI model that accurately predicts shear strength parameters of municipal solid waste, providing transparent insights into how waste composition influences geotechnical properties.

Contribution

The study presents a novel XAI framework combining neural networks and SHAP analysis for interpretable prediction of MSW shear strength parameters, outperforming traditional methods.

Findings

Model achieved mean absolute percentage errors of 7.42% for friction angle and 14.96% for cohesion.

SHAP analysis identified fibrous materials and particle size as key strength influencers.

The approach enables rapid, interpretable assessment of waste mechanical properties.

Abstract

Accurate prediction of shear strength parameters in Municipal Solid Waste (MSW) remains a critical challenge in geotechnical engineering due to the heterogeneous nature of waste materials and their temporal evolution through degradation processes. This paper presents a novel explainable artificial intelligence (XAI) framework for evaluating cohesion and friction angle across diverse MSW compositional profiles. The proposed model integrates a multi-layer perceptron architecture with SHAP (SHapley Additive exPlanations) analysis to provide transparent insights into how specific waste components influence strength characteristics. Training data encompassed large-scale direct shear tests across various waste compositions and degradation states. The model demonstrated superior predictive accuracy compared to traditional gradient boosting methods, achieving mean absolute percentage errors of 7.42% and 14.96% for friction angle and cohesion predictions, respectively. Through SHAP analysis, the study revealed that fibrous materials and particle size distribution were primary drivers of shear strength variation, with food waste and plastics showing significant but non-linear effects. The model's explainability component successfully quantified these relationships, enabling evidence-based recommendations for waste management practices. This research bridges the gap between advanced machine learning and geotechnical engineering practice, offering a reliable tool for rapid assessment of MSW mechanical properties while maintaining interpretability for engineering decision-making.