Faculty Publications
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Item Review on Landslide Early Warning System: A Brief History, Evolution, and Controlling Parameters(Springer Science and Business Media Deutschland GmbH, 2022) Menon, V.; Kolathayar, S.A landslide is a life-threatening event causing large infrastructural damages. The major causes for landslides are rainfall, earthquakes, blasting, and other man-made activities. The landslides can happen even without human interference, and hence, it can be classified as a natural process. The population expansion in landslide-prone areas demands a better sustainable method for slope protection and landslide prediction. The main objective of this chapter is to review the previous research studies conducted in the soil slopes using manual instrumentations and the improved data acquisition systems (DAQ) that are used recently. A landslide early warning system (LEWS) is only as strong as the understanding of the phenomenon. The first objective of the development of an early warning system is to find the triggering mechanism of the impending disaster. The three landslide triggering mechanisms are excess rain, seismic activities, and man-made activities. For rainfall-induced landslides, finding the rainfall threshold parameter is one of the difficult tasks. The methods involved in finding this threshold and using it for the development of the LEWS will be discussed in this chapter. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Geohazard Investigation and Management: An Introduction(Springer Science and Business Media Deutschland GmbH, 2022) Adhikari, B.R.; Menon, V.; Kolathayar, S.The understanding of the systemic risk posed by multi-hazard is becoming a new area of research and adopted by the different global and national frameworks. This chapter summarizes the contents of the book titled geohazard mitigations, which contains thirty-eight chapters. These chapters are divided into three sections namely: multi-hazard assessment, landslide hazard assessment and mitigation, and geotechnical engineering. This chapter also includes basic definitions and introduction to geohazard investigation and management. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Review of Experimental Studies on Rainfall-Induced Landslide Using the Laboratory Flume Technique(Springer Science and Business Media Deutschland GmbH, 2024) Menon, V.; Kolathayar, S.Rainfall-induced landslides are one of the most influential disasters in the monsoon season for rainfall-rich parts of India, primarily southern and northeastern. Landslides are geotechnical phenomena that can be studied by conducting field experiments, laboratory experiments, and numerical modeling. Various methods can be adopted to study landslides, and the landslide flume seems to be one of the most widely used techniques for laboratory simulation of the landslides. The scaled models created in a flume can represent the real-world situations so far, which will be reviewed here. The essential aspects of this review include the practicality of the flume experiments from the previous findings, how they can be related to the real-world scenarios, the difference between field instrumentations and laboratory instrumentations, discrepancies that can occur due to the scaled effect of laboratory simulation, and recommended techniques for the future development of laboratory simulated landslides with tilting type flume incorporated with a rainfall simulator. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Numerical Analysis of Geocell Retaining Wall for Slope Stabilization of Netravathi River Bank(Springer Science and Business Media Deutschland GmbH, 2025) Anjana, S.; Menon, V.; Kolathayar, S.Stability of slopes is a major concern when it comes to construction near hills, excavations, embankments and river banks. Geosynthetics and geotextiles have proven to be extremely efficient in slope stabilization and landslide protection, among which the use of geocells has been drastically increasing. It is made up of geogrid like materials that give it sufficient tensile strength. This paper focuses on the use of geocells to construct a retaining wall to support an existing backfill. The study area is located at a Netravathi River bank which belongs to a temple in South Mangalore city. The aim of the work is to prevent flooding of the temple during heavy rainfall and the erosion of banks. Numerical analyses have been carried out by Limit Equilibrium Method (LEM) using GEO5 software and Finite Element Method (FEM) using PLAXIS 2D software. The results of displacements and factor of safeties have been compared for both methods to determine if the proposed structure is stable. Geocell retaining wall has been developed in PLAXIS 2D using a soil geocell equivalent composite model due to the difficulties of modelling its actual honeycomb like structure. The structure gives sufficient safety for static as well as pseudo static condition for different water levels hence preventing further erosion of existing soil. The design provided for the present work gives a factor of safety of 1.69 in LEM analysis and 1.90 in FEM analysis for the rapid drawdown condition in a psuedo dynamic analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.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 Rainfall-Induced Slope Instability in a Tilting Flume: Analysis of Pore Pressure Variations and Surface Crack Percentage(Springer, 2025) Menon, V.; Kolathayar, S.This study investigates the relationship between surface crack development and excess pore water pressure (EPWP) during rainfall-induced debris flow conditions. A custom-designed tilting flume integrated with a rainfall simulator was fabricated to replicate slope failure scenarios. Silty sand was tested under controlled conditions on 45° and 60° slopes with identical rainfall intensities. Surface cracks were quantified using an image processing algorithm to calculate crack percentages, and real-time EPWP measurements were recorded to assess their correlation. The results demonstrate that surface crack formation significantly influences EPWP, suggesting a potential interdependence between these parameters. Furthermore, the study evaluates whether EPWP can serve as an effective threshold parameter for landslide early warning systems (LEWS). These findings contribute to a better understanding of landslide mechanics and provide critical insights for enhancing LEWS design and implementation. © The Author(s), under exclusive licence to Indian Geotechnical Society 2025.Item Slope Stability Analyses and Design for a Telecommunication Tower Site in Kodagu—Limit Equilibrium and Finite Element Approach with Spatial Data Integration(Springer Science and Business Media Deutschland GmbH, 2025) Menon, V.; Anjana, S.; Kolathayar, S.This paper examines slope stability issues at the All-India Radio Telecommunication tower site in Kodagu, Coorg, Karnataka, India, where the hillock on which the tower stands has shown signs of instability after the monsoon of 2022. This study proposes reclamation strategies to mitigate future landslips in the region. A spatial analysis utilizing open-source Digital Elevation Models and Scoop3D software was performed to identify critical locations prone to landslips. The designs were assessed using the Limit Equilibrium method (LEM) and Finite Element method (FEM). Both static and pseudo-static conditions were considered in the analyses, with and without reinforcement, using the Limit Equilibrium Method and Finite Element Method. The proposed design aligns with the United Nations Sustainable Development Goals (SDGs) 9 and 11, demonstrating a significant increase in the Factor of Safety by more than 10%. The study recommends a geocell-based hybrid retaining system as a comprehensive solution to enhance slope stability and protect the site from future landslips. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Item Landslides and debris flow triggered by the July 2024 extreme rainstorm in the Chooralmala watershed in Wayanad, India(Springer Science and Business Media Deutschland GmbH, 2025) Kolathayar, S.; Menon, V.; Kundu, P.[No abstract available]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.