Artificial intelligence-based prediction models of bio-treated sand strength for sustainable and green infrastructure applications

• Published in: Transportation Geotechnics Vol. 46; p. 101262
• Main Authors: Naqeeb Nawaz, Muhammad, Yar Akhtar, Ahmed, Hassan, Waqas, Hasnain Ayub Khan, Muhammad, Muneeb Nawaz, Muhammad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024; Elsevier
• Subjects: Artificial intelligence; Eco-friendly solution; Engineering Sciences; Machine learning; Microbially induced calcite precipitation; Sustainable infrastructure; Microbially induced calcite precipitation; Eco-friendly solution; Artificial intelligence; Sustainable infrastructure; Machine learning
• ISSN: 2214-3912; 2214-3912
• DOI: 10.1016/j.trgeo.2024.101262

•Micro-level review of soil strength enhancement mechanism of MICP is presented.•The strength induced by MICP is predicted via advanced ML approaches.•The multiple influencing factors are considered for model development.•The proposed prediction models predict UCSmicp with higher accuracy. As a sustainable, environmentally friendly, and green solution, biomineralization/bio-treatment process has recently emerged for enhancing the strength characteristics of loose sands by inducing cementation material commonly referred to as microbially induced calcite/cementation precipitation (MICP). To date, existing research on the unconfined compression strength of MICP-treated sands (UCSmicp) relies heavily on experimental data, with predictions primarily based on linear or curvilinear models that consider calcite (CaCO3) as the main influencing factor. However, there is limited exploration into the application of artificial intelligence (AI) and machine learning (ML) methods for UCSmicp prediction involving multiple influencing factors and their validation on unseen laboratory data. Therefore, this study intends to propose new prediction models of UCSmicp using four advanced ML techniques: Gaussian Process Regression (GPR), Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Gene Expression Programming (GEP). The models incorporate various factors affecting UCSmicp, including sand uniformity coefficient (Cu), initial void ratio (e), mean grain size (D50), optical density at 600 mm wavelength (OD600), urea concentration (U), calcium concentration (Ca), and calcite content (C). Performance evaluation of models involved statistical assessments, error analysis, cross validation on laboratory data, and sensitivity and monotonicity studies. Results indicate that the proposed models exhibit exceptional precision across training, testing, and validation data in predicting UCSmicp, with ANN demonstrating the highest accuracy, followed by GPR, ANFIS, and GEP. Sensitivity analysis identifies CaCO3 as the main influencing factor on UCSmicp, while monotonicity analysis confirms alignment with physical processes. This work advocates for MICP as an eco-friendly solution and highlights the efficacy of advanced ML techniques in fostering sustainable infrastructure.