S. Elayaraja

Bio: S. Elayaraja is an academic researcher from VIT University. The author has contributed to research in topics: Landslide & Peak ground acceleration. The author has an hindex of 2, co-authored 2 publications receiving 22 citations.

Damages to Transport Facilities by Rainfall Induced Landslides During November 2009 in Nilgiris, India

Abstract: Nilgiris district, a renowned hill station in the state of Tamilnadu, India, has a history of damaging landslides. This paper presents field observations and results of preliminary geotechnical investigations on the damages to infrastructure due to rainfall induced landslides during November 2009, which left more than fifty people dead and hundreds homeless. Laboratory tests were conducted on soil samples collected from landslide locations. The soils are of clayey sand and silty sand type with high fine content and low permeability. Case studies of damages caused to roads, railways and buildings are presented. Though the immediate triggering factor for the landslide at many locations was heavy intense rainfall, there were several causal factors like excavation of slope at toe, vertical cutting, loading at crest and defective maintenance of surface drainage systems.

Evaluation of seismic hazard and potential of earthquake-induced landslides of the Nilgiris, India

Abstract: The Nilgiris district in the Tamilnadu state of India is frequented by many landslides in the recent past. Though many of these landslides are rainfall-induced, there is a need to evaluate the potential of earthquake-induced landslides considering seismicity of the region. In this paper, deterministic seismic hazard of Nilgiris is carried out by considering a study area of 350 km radius around Nilgiris. Seismotectonic map of the Nilgiris, showing the details of faults and past earthquakes, is prepared. The peak ground acceleration (PGA) at bed rock level and response spectrum are evaluated. The potential sources for Nilgiris are Moyar and Bhavani shears. The PGA at bed rock level is 0.156 g corresponding to maximum considered earthquake 6.8. Ground response analysis for seven sites, in the Nilgiris, is carried out by one-dimensional equivalent linear method using SHAKE 2000 program after considering the effect of topography. PGA of surface motion got amplified to 0.64 g in Coonoor site and 0.44 g in Ooty site compared to 0.39 g of the input motion. The bracketed duration of time history of surface acceleration has increased to 20 s in Coonoor site and 18 s in Ooty site compared to that of 8 s of input motion. Results from seismic displacement analysis using Newmark’s method revealed that out of seven sites investigated, five sites have moderate seismic landslide hazard and two sites (Coonoor and Ooty) have high hazard.

Investigation on infrastructural damages by rainfall-induced landslides during November 2009 in Nilgiris, India

Abstract: India is among the top ten countries with the highest percentage of landslide fatalities for the past few years. Intense rainfall during the 2009 monsoon in the hilly district of Nilgiris, in the state of Tamilnadu in India, triggered landslides at more than 300 locations which affected road and rail traffic, destroyed buildings, caused the death of more than 40 people and left hundreds homeless. In this paper, three case histories from Nilgiris district are investigated: the slope failure of a railway track at Aravankadu, failure of retaining walls supporting buildings at Coonoor, failure of the slope and retaining wall along national highway (NH67) at Chinnabikatty. Laboratory investigations are carried out on soil samples collected at these sites. Soils at all the three locations have high fine content and low values of coefficient of permeability. Finite element analyses of all the three case histories were carried out using PLAXIS2D software in order to understand the failure mechanism and contributing factors. Slope stability analysis using strength reduction technique is carried out for the slope at Aravankadu to determine the critical slip surface and factor of safety. Results reveal that the increase in pore pressures led to a reduction in shear strength of the soil and consequently resulted in progressive failure of slope at Aravankadu site. Displacement analysis is carried out for Coonoor and Chinnabikatty sites. The results show that combined effect of surcharge load of building and high pore pressure led to intense shearing behind the retaining walls at Coonoor site. Results indicate occurrence of large displacements along the face and at toe of the slope at Chinnabikatty site.

Performance comparison of artificial neural network models for daily rainfall prediction

Abstract: With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

Geotechnical characterization and analysis of rainfall—induced 2009 landslide at Marappalam area of Nilgiris district, Tamil Nadu state, India

Abstract: Landslides are frequently occurring natural hazards in Nilgiris district of Tamil Nadu, India, particularly during monsoon season The present study describes the geotechnical characterization and analysis of rainfall-induced landslide that occurred at Marappalam location of Nilgiris district on November 10, 2009 The detailed investigation comprises mapping of landslide, topographical survey, field and laboratory investigations, characterization of soil and rock, and numerical analysis Field study comprises borehole and geophysical investigations Detailed laboratory investigation was performed to identify index and engineering properties of soil and rock Based on the results obtained from field and laboratory investigations, the generalized subsoil profile of Marappalam slope has been plotted The investigations revealed that loose and soft soil layer with low permeability and plasticity interspersed with boulders could be the main source of debris flow Scanning electron microscopic analysis and x-ray diffraction analysis were performed to study the influence of weathering on slope failure Failure mechanism of Marappalam 2009 landslide was identified from the numerical analysis performed using landslide simulation program LS-RAPID The analysis revealed that the 5-day antecedent rainfall (303 mm) and intense rainfall on 10th November 2009 (405 mm) saturate the slope due to infiltration of rainwater This leads to a decrease in the matric suction and subsequent development of positive pore water pressure, which in turn reduces the shear resistance of the soil along with shear displacement, and resulted in a progressive failure

The performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery

Abstract: An earthquake with Ms 7.0 (33.2° N, 103.8° E) occurred in Jiuzhaigou County of Sichuan Province in China on 8 August 2017. This earthquake triggered a large number of landslides in the study area. Although the susceptibility quality level index has improved, the high-quality assessments still have remained rare. We adopted three models, including the logistic regression (LR), support vector machine (SVM), and random forest (RF) to study the quality performance of the susceptibility distribution rule of earthquakes induced landslides. We used satellite images of before and after earthquakes and landslides as well. We used the area under receiver operating characteristic (ROC) curve (AUC) and ratio to evaluate the model’s accuracy and quality performance, including the mapping availability susceptibility assessment. This study reveals that RF has the highest ratio (2.07) as compared to the LR (1.78) and SVM (1.90). The result shows that RF has more potential to implement future experiments in Sichuan Province because of a better performance quality in the susceptibility assessment of landslides induced by earthquakes.