Faculty Publications
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Item Optimizing nailing parameters for hybrid retaining systems using supervised learning regression models(Springer Science and Business Media B.V., 2024) Menon, V.; Kolathayar, S.The work focuses on creating a hybrid retaining wall using geocell, geogrid, and soil-nailing techniques for a road embankment in Mangalore, India. Soil nailing reinforces the soil, geogrids give extra support, and geocell serves as a protective facia against external weathering impacts, decreasing the requirement for conventional shotcreting and lowering the carbon footprint of concrete. This promotes the United Nations’ Sustainable Development Goals (SDGs). The usage of concrete and steel in soil nailing can be minimized using supervised learning regression models (SLRMs), a branch of machine learning (ML). The soil properties in the site were collected by standard penetration tests (SPT). From the limit equilibrium method (LEM) study, 600 iterations are carried out to estimate the factor of safety (FoS), which serves as input training and testing data for the ML model. The surrogate model produces findings for the entire site to identify ideal nail parameters. The random forest (RF) model was found to be useful with a mean square error (MSE) value of 0.009. The finite element method analysis (FEM) yields a modest overestimation of roughly 4.5% while validating the results of the RF model in a typical slope. This study demonstrates the practical application of sustainable methodologies and machine learning to meet crucial development goals, explicitly improving slope stability and road development in the study area through environmentally conscious engineering practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Item Analysis and Design of a Hybrid Reinforced Earth Retention System for Sustainable Slope Protection: A Case Study Using Limit Equilibrium and Finite Element Methods(Springer, 2025) Menon, V.; Kolathayar, S.This study proposes an innovative hybrid earth retention system to stabilize slopes for a road-widening project in Dakshina Kannada, Karnataka, India. The system combines soil nailing, geogrid reinforcement, geocell walls, and biotechnical stabilization—popular geotechnical techniques aligned with sustainable development goals. These methods were engineered synergistically to address the site-specific challenges of restoring a slope that experienced five major collapses during heavy rains, enabling both highway expansion and slope protection without disrupting traffic flow. Soil samples were collected, and laboratory tests were conducted to evaluate the engineering properties of the site soil. Boreholes were drilled at strategic locations and Standard Penetration Tests were performed. The analysis and design of the retention system employed both the Limit Equilibrium Method (LEM) and the Finite Element Method (FEM), utilizing GEO5 and OptumG2 software, respectively. A comparative analysis of these methods is presented, along with a non-linear regression model to establish correlations for soil nail parameters derived from LEM analyses. The study demonstrates the successful integration of geocell walls with soil nailing and geogrid reinforcement to support an unprotected embankment. The findings include the site reconnaissance report, reclamation strategies, and a detailed discussion of LEM and FEM analysis results, establishing the robustness and sustainability of the proposed hybrid retention system. © The Institution of Engineers (India) 2025.Item Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India(Springer Nature, 2025) Menon, V.; Kolathayar, S.Kerala, a state in India, experiences one of the highest incidences of rainfall-induced landslides. Historical data has been collected and analyzed to devise thresholds for the early detection of landslides. Two empirical approaches based on the relationships between rainfall intensity and duration, as well as cumulative rainfall and duration, have been utilized to identify early warning thresholds for landslides. Five machine learning-based approaches were employed to determine these thresholds. Among the classifiers tested, the K-Nearest Neighbour (KNN) classifier with K=5 demonstrated the highest prediction accuracy compared to other methods in the study.; For the safe and resilient development of cities, disaster risk reduction plays a crucial role, aligning with sustainable development goal 11 of the United Nations. Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. Using a dataset of 64 rainfall-induced landslide events recorded since the year 2000, rainfall data were collected up to 15 days prior to each landslide to support empirical analysis of intensity-duration and event rainfall-duration thresholds. In cases where exact rainfall durations were unavailable, classification machine learning (ML) models, including K-nearest neighbours (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and logistic regression, were used to determine threshold reliability. Among these, the KNN model with 5 Neighbours achieved the highest performance, with an ROC-AUC of 0.9 and an accuracy of 82%. This model, saved as a pickle file, serves as a core filter in the development of a landslide early warning system. This paper presents the model development and performance comparisons, contributing to a practical, community-centred solution for landslide disaster resilience in Kerala. © The Author(s) 2025.; © The Author(s) 2025.Item Experimental Evaluation of Geocoir Cell-Reinforced Sand Beds with Different Infill Materials(Springer Science and Business Media Deutschland GmbH, 2025) Namburu, S.K.; Venkateswarlu, H.; Kolathayar, S.Exploring sustainable alternatives to polymeric materials for reinforcement applications has gained considerable momentum in recent years. This research explores the use of locally sourced, bio-based materials, specifically geocoir cell mattresses and coconut shell infills, to offer eco-friendly and cost-effective reinforcement solutions. The methodology involves conducting model plate load tests on sand beds, both unreinforced and reinforced with geocoir cell mattresses filled with either coconut shell pieces or sand. The primary objective is to evaluate the load-settlement behaviour and load-bearing capacity of these reinforced beds. The study demonstrates that geocoir cell-reinforced sand beds, when filled with coconut shell pieces, exhibit significantly enhanced load-settlement behaviour compared to traditional sand infill. The results indicate that coconut shell infill increases load-bearing capacity by up to 4.98 times and reduces settlement by 88%. These findings demonstrate the potential of geocoir cells and coconut shells as eco-friendly, cost-effective alternatives for soil reinforcement in construction. The benefit of this study is understanding performance benefits of geocoir cell with coconut shell infill and utilizing such material in for reinforcement applications reduce the reliance on synthetic polymers and enhance the geotechnical performance in terms of extending the service life and minimizing the maintenance costs. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.