Search

DFOS (distributed fiber-optic sensing) technology has shown the potential to increase the accuracy of measurement after years of development and experimenting in geoengineering monitoring. To better understand the development of DFOS technology and its contribution to geoengineering, an objective and data-driven review of the development process of DFOS technology in construction was completed. The review was accomplished by using text mining methods on the Web of Science, covering a wide range of relevant data, including 3970 articles from 1989 to 2023. The results indicate that DFOS technology research demonstrates the typical characteristics of multi-author, multi-country, and multi-institution collaborations, spanning various research fields. Over the past 35 years, the number of published articles has exhibited exponential growth, with China making significant contributions and leading in terms of its total publication growth rate, which has been higher than that of the United States since 2016. In the analysis of author keywords, emerging technologies, such as machine learning and distributed acoustic sensing, have garnered attention. The findings contribute to a comprehensive understanding of the development, impact, and future trends of DFOS technology in geotechnical engineering, offering valuable insights for researchers, scholars, and students in the field and inspiring new approaches for research methods in this domain.

Debris flows are rapid mass movements with a mixture of rock, soil and water. High-intensity rainfall events have triggered multiple debris flows around the globe, making it an important concern from the disaster management perspective. This study presents a numerical model called debris flow simulation 2D (DFS 2D) and applicability of the proposed model is investigated through the values of the model parameters used for the reproduction of an occurred debris flow at Yindongzi gully in China on 13 August 2010. The model can be used to simulate debris flows using three different rheologies and has a user-friendly interface for providing the inputs. Using DFS 2D, flow parameters can be estimated with respect to space and time. The values of the flow resistance parameters of model, dry-Coulomb and turbulent friction, were calibrated through the back analysis and the values obtained are 0.1 and 1000 m/s2, respectively. Two new methods of calibration are proposed in this study, considering the cross-sectional area of flow and topographical changes induced by the debris flow. The proposed methods of calibration provide an effective solution to the cumulative errors induced by coarse-resolution digital elevation models (DEMs) in numerical modelling of debris flows. The statistical indices such as Willmott's index of agreement, mean-absolute-error, and normalized-root-mean-square-error of the calibrated model are 0.5, 1.02 and 1.44, respectively. The comparison between simulated and observed values of topographic changes indicates that DFS 2D provides satisfactory results and can be used for dynamic modelling of debris flows.

The increase in population and urbanisation of hilly regions have increased the risk due to landslides. This manuscript presents a data-driven approach with a random forest algorithm to estimate the projected area, length, travel distance, and width of landslides, using elevation and slope information. The method is tested for two different study areas (Idukki and Wayanad), using three different combinations of inputs. The input features considered were elevation ([Formula: see text]), tangential slope ([Formula: see text]), drop height ([Formula: see text]), angle of reach ([Formula: see text]) and the profile curvature ([Formula: see text]). A total of 144 models were considered and were evaluated using mean-absolute-error ([Formula: see text]) and root-mean-square-error (RMSE) values. The results indicate that, by using E and θ alone, the [Formula: see text] value in estimating the length values for flow-like landslides in Wayanad was reduced from 472.74 m to 204.64 m. Out of the 48 combinations considered, [Formula: see text] values have increased in seven cases and [Formula: see text] values in eight cases only. The pre-trained models are saved and used to develop an easy-to-use tool, which can bypass the complications associated with the existing statistical approaches. The tool can be used by untrained personnel for preliminary hazard assessment.

Wind erosion is one of the significant natural calamities worldwide, which degrades around one-third of global land. The eroded and suspended soil particles in the environment may cause health hazards, i.e. allergies and respiratory diseases, due to the presence of harmful contaminants, bacteria, and pollens. The present study evaluates the feasibility of microbially induced calcium carbonate precipitation (MICP) technique to mitigate wind-induced erosion of calcareous desert sand (Thar desert of Rajasthan province in India). The temperature during biotreatment was kept at 36 °C to stimulate the average temperature of the Thar desert. The spray method was used for bioaugmentation of Sporosarcina (S.) pasteurii and further treatment using chemical solutions. The chemical solution of 0.25 pore volume was sprayed continuously up to 5 d, 10 d, 15 d, and 20 d, using two different concentration ratios of urea and calcium chloride dihydrate viz 2:1 and 1:1. The biotreated samples were subjected to erosion testing (in the wind tunnel) at different wind speeds of 10 m/s, 20 m/s, and 30 m/s. The unconfined compressive strength of the biocemented crust was measured using a pocket penetrometer. The variation in calcite precipitation and microstructure (including the presence of crystalline minerals) of untreated as well as biotreated sand samples were determined through calcimeter, scanning electron microscope (SEM), and energy-dispersive X-ray spectroscope (EDX). The results demonstrated that the erosion of untreated sand increases with an increase in wind speeds. When compared to untreated sand, a lower erosion was observed in all biocemented sand samples, irrespective of treatment condition and wind speed. It was observed that the sample treated with 1:1 cementation solution for up to 5 d, was found to effectively resist erosion at a wind speed of 10 m/s. Moreover, a significant erosion resistance was ascertained in 15 d and 20 d treated samples at higher wind speeds. The calcite content percentage, thickness of crust, bulk density, and surface strength of biocemented sand were enhanced with the increase in treatment duration. The 1:1 concentration ratio of cementation solution was found effective in improving crust thickness and surface strength as compared to 2:1 concentration ratio of cementation solution. The calcite crystals formation was observed in SEM analysis and calcium peaks were observed in EDX analysis for biotreated sand.

Abstract The rapid development of infrastructure often encounters the loose subgrades and is becoming difficult to carry to construction activities. Numerous counteracting methods are developed to control the swelling-shrinkage behavior of the expansive subgrades. The mechanical stabilization of the expansive subgrades by reinforcing with the polypropylene fiber and geogrid is sustainable. Geogrids and polypropylene fibers have been used extensively to strengthen the expansive subgrade and foundations as individuals. The polypropylene fiber reinforcement enhanced the reinforced expansive subgrades's tensile strength capacity, wherein the geogrid reinforcement is the quick fix mechanical stabilization technique, which reduces the pavement failures. In this research, the polypropylene fiber and geogrid reinforcement’s combined effect has been evaluated to stabilize the pavement subgrades. The various mechanical strength test such as unconfined compressive strength (UCS) and large direct shear box test was conducted to evaluate the mechanical interaction between expansive subgrades, polypropylene fiber, triaxial geogrid, and biaxial geogrid at the interface. The polypropylene fiber of 12 mm length was used in the proportion of 0.25%, 0.5%, and 1.0% and single geogrid layer at mid-depth. The result shows that reinforced subgrades’ shear strength with a layer of biaxial/ triaxial geogrid and polypropylene fiber increases by 177%. It is also observed that the unconfined compressive strength of the expansive subgrades increased ranging 3.8–139.6% with the inclusion of polypropylene fiber with geogrid in different combinations. The combined reinforcement method shows an effective treatment methodology to improve the property of expansive subgrades.

Co-seismic landslides are the most dangerous geological hazards in seismically active mountainous regions. These landslides cause damage to roads, drainage pipelines, buildings, agriculture, and loss of human lives. It is crucial but difficult to precisely map the hazards resulting from earthquake-induced landslides. Newmark's approach is widely used to determine the permanent displacement of a probable sliding mass and simulate the mechanism of seismically-induced slope failure. The present study assesses co-seismic landslide hazards for Uttarakhand state (India), which falls under the highest seismically active zones as per the seismic code of India. It proposes an improved Newmark’s approach aby incorporating rock joint shear strength parameters and the size effect of possible sliding surfaces. Static factor of safety and critical acceleration maps were prepared for the study area by combining various geological, geotechnical, and topographical parameters. The ground motion parameters were determined in terms of surface-level Arias Intensity. Newmark permanent displacement map was prepared by taking critical acceleration and Arias Intensity as input parameters. Finally, the modified Newmark’s model results were compared with the conventional Newmark’s model using the area under curve (AUC) analysis. This assessment approach can be used effectively to forecast the area affected by co-seismic landslide hazards and prepare guidelines for major infrastructure projects and post-disaster damage studies.

This study explores the coupling effect of pond ash (PA) and polypropylene (PP) fiber to control the strength and durability of expansive soil. The PA is used to chemically treat the expansive soil and PP fiber is adopted as reinforcement against tensile cracking. The sustainable use of PA and PP fiber are demonstrated by performing mechanical (i.e. unconfined compressive strength, split tensile strength and ultrasonic pulse velocity), chemical (pH value, electrical conductivity and calcite content), and microstructural analyses before and after 2nd, 4th, 6th, 8th and 10th freezing-thawing (F-T) cycles. Three curing methods with 7 d, 14 d and 28 d curing periods are considered to reinforce the 5%, 10%, 15% and 20% PA-stabilized expansive soil with 0.25%, 0.5% and 1% PP fiber. In order to develop predictive models for mechanical and durability parameters, the experimental data are processed utilizing artificial neural network (ANN), in association with the leave-one-out cross-validation (LOOCV) as a resampling method and three different activation functions. The mechanical and durability properties of the PA-stabilized expansive soil subgrades are increased with PP fiber reinforcement. The results of ANN modeling predict the mechanical properties perfectly, and the correlation coefficient (R) approaches up to 0.96.

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

Highlights The nonlinear displacement mitigation of multilayered soils was numerically investigated. The statistical model supports minimization and verification numerical simulation. The multilayered soil arrangement has a significant impact to minimize the nonlinear displacement of multilayered soils.

Abstract This study explored the effect of indigenous bacteria present in the soil to stabilized swelling behavior and improving the mechanical property of expansive soil. The objective of the research is to investigate the effectiveness of the biostimulation microbial induced calcite precipitation (MICP) for controlling the swelling-shrinkage behavior and improving shear strength of expansive soil. An attempt was made to develop an effective procedure to culture the indigenous bacteria for treating clays with varying plasticity and improve their engineering behavior. The detailed procedure has been investigated to effectively apply the MICP technique in clay soil, considering its low permeable nature. The applicability of biostimulation to clayey soils in minimizing their swelling potential and improving the strength is assessed. Both macroscale and microscale studies were conducted on untreated and biostimulated soils to observe changes in plasticity, strength, swelling, mineralogical, chemical characteristics. The present method has shown an effective alternative to improve the road pavement subgrade without affecting the eco-system of natural soil. The method investigated the effective way of providing the enrichment and cementation solution in clayey soil, which is the major concern in current literature. The study confirms that the calcite content has been increased with biostimulated MICP treatment up to 205% in the treated specimens and which future increased the unconfined compressive strength and split tensile strength. A reduction in the swelling pressure and swell strain is also observed. The results show that a cost-effect and eco-friendly method can be deployed for stabilizing the road pavement subgrades. The statistical assessment using multivariate analysis and hierarchical clustering dendrogram has been carried out to investigate the effect of the MICP treatment protocol on different soil and engineering parameters.

This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient RCV of 0.9988.

This study proposes a regional landslide early warning system for Idukki (India), using a decisional algorithm. The algorithm forecasts the possibility of occurrence of landslide by comparing the rainfall thresholds with the cumulated rainfall values. The region has suffered severe socio-economic setbacks during the disastrous landslides that happened in 2018 and 2019. Rainfall thresholds are defined for Idukki, using the total amount of precipitation cumulated at different time intervals ranging from 1 to 30 days. The first three-day cumulative values were used for evaluating the effect of short-term rainfall and the remaining days for the effect of long-term rainfall. The derived thresholds were calibrated using historical landslides and rainfall data from 2009 to 2017, optimized to reduce the false alarms and then validated using the 2018 data. The validation results show that the model is effectively predicting 79% of the landslides that happened in the region during 2018 and can be easily integrated with a rainfall forecasting system for the prediction of landslides. The model can be further improved with the availability of better spatial and temporal resolution of rainfall data and can be used as an effective tool for predicting the occurrence of landslides.

With the increasing computational facilities and data availability, machine learning (ML) models are gaining wide attention in landslide modeling. This study evaluates the effect of spatial resolution and data splitting, using five different ML algorithms (naïve bayes (NB), K nearest neighbors (KNN), logistic regression (LR), random forest (RF) and support vector machines (SVM)). The maps were developed using twelve landslide conditioning factors at two different resolutions, 12.5 m and 30 m. To identify the effect of data splitting on model performance, 2162 landslide points and an equal number of non-landslide points were used for training and testing the models using k-fold cross-validation, by varying the number of folds from two to ten. Results indicated that the spatial resolution of the dataset affects the performance of all the algorithms considered, while the effect of data splitting is significant in KNN and RF algorithms. All the algorithms yielded better performance while using the dataset with 12.5 m resolution for the same number of folds. It was also observed that the accuracy and area-under-the-curve values of 7, 8, 9, and 10-fold cross-validations with 30 m resolution was better than 2 and 3-fold cross-validations using 12.5 m resolution, in the case of RF algorithm.

Structures constructed on expansive soils are experiencing higher upward pressure due to swelling- shrinkage nature of expansive soil. India has 15–20% expansive soil, which causes severe damage to the existing structure, including pavements, dams, multi-storey buildings, retaining walls. The aim of the study is to suitably reduce the distressed caused by the expansive soil on paved structures. The experimental result of expansive soil subgrade reinforced with and without coir geotextile mat has been presented in this paper. Lime and silica fume, treated and untreated coir geotextile mat with polypropylene fiber net has been used. A single and double layer of coir geotextile mat at various depths have been placed to evaluate the effect of reinforcement on expansive soil subgrade. The treatment was carried out to enhance the efficiency of coir geotextile for reducing the swelling pressure of the expansive soil subgrade. The chemical and microstructural components of expansive soil samples were investigated by using X-ray Diffraction (XRD) and Fourier Transform Infrared (FTIR). The constant volume swelling pressure, California Bearing Ratio (CBR) and large size direct shear (300 mm × 300 mm × 150 mm) tests have been carried out to evaluate the effect of treated and untreated coir geotextile on expansive soil subgrade. The experimental results analyzed indicates that the lime treated coir geotextile mat reduces the upward swelling pressure by 52.19% in single-layer and 81.89% in double-layer. The tangible improvement in the shear strength and bearing capacity of expansive soil subgrade was observed with the inclusion of lime treat coir geotextile. A lime treated coir geotextile in a single or double-layer can be used to stabilize the expansive soil subgrade. The improved property of reinforced expansive soil subgrade can reduce the thickness of the subbase and base course layer in paved structure.

In recent years, due to the reduction in available natural resources, the attention of many researchers has been focused on the reuse of recycled materials and industrial waste in common engineering applications. This paper discusses the feasibility of using seven different materials as alternative fillers instead of ordinary Portland cement (OPC) in road pavement base layers: namely rice husk ash (RHA), brick dust (BD), marble dust (MD), stone dust (SD), fly ash (FA), limestone dust (LD), and silica fume (SF). To exclusively evaluate the effect that selected fillers had on the mechanical performance of asphalt mixtures, we carried out Marshall, indirect tensile strength, moisture susceptibility, and Cantabro abrasion loss tests on specimens in which only the filler type and its percentage varied while keeping constant all the remaining design parameters. Experimental findings showed that all mixtures, except those prepared with 4% RHA or MD, met the requirements of Indian standards with respect to air voids, Marshall stability and quotient. LD and SF mixtures provided slightly better mechanical strength and durability than OPC ones, proving they can be successfully recycled as filler in asphalt mixtures. Furthermore, a Machine Learning methodology based on laboratory results was developed. A decision tree Categorical Boosting approach allowed the main mechanical properties of the investigated mixtures to be predicted on the basis of the main compositional variables, with a mean Pearson correlation and a mean coefficient of determination equal to 0.9724 and 0.9374, respectively.

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.

The Himalayan region with its complex geological features, steep slopes, rugged topography, and intense monsoonal rainfall create ideal conditions for landslides. The damage caused due to landslides is immense causing significant loss of life and property initiating a dire need to formulate strategies in minimizing its impact in areas affected by landslides. Several attempts have been presented by various researchers in order to establish rainfall intensity thresholds using various parameters of rainfall conditions. These methods provide deterministic thresholds, i.e., landslide or no landslide; such thresholds are not always suited for landslides. In this paper, rainfall thresholds have been evaluated using a statistical method which results in the probability of landslide occurrence for single or multiple rainfall parameters leading to slide initiation. The results are expressed in terms of probabilities by analyzing two different variants of Bayes theorem, i.e., 1D and 2D. Probabilistic thresholds were calculated for Kalimpong region of Darjeeling Himalayas using available rainfall and landslide data during the year 2010–2016. The probabilities calculated for landslide occurrence were found to be 0.37 for rainfall intensity greater than 10 mm/day. However, the probability for a combination of rainfall intensity of 30 mm/day with duration of 3 days was calculated to be 0.67. The results also depicted that landslides are related to rainfall event parameters especially with rainfall intensity.

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

Expansive soil shows dual swell–shrink which is not suitable for construction. Several mitigating techniques exist to counteract the problem promulgated by expansive clayey soils. This paper explored the potential mecho-chemical reinforcement of expansive clayey soil to mitigate the effect of upward swelling pressure and heave. The polypropylene fiber is randomly distributed in the soil for mechanical stabilization, and the industrial residual silica fume is used as a chemical stabilizer. The experimental analysis was made in three phases which involved tests on mechanically-reinforced expansive soil, using randomly distributed polypropylene fibers with different percentages (0.25%, 0.50%, and 1.00%), and which were 12 mm length. The second phase of experiments was carried out on chemical stabilized expansive soil with different percentages (2%, 4%, and 8%) of silica, and the next phase of the experiment focused on the combination of mecho-chemical stabilization of the expansive soil with different combinations of silica (i.e., 2%, 4%, and 8%) and polypropylene fibers (i.e., 0.25%, 0.50%, and 1.00%). Maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plastic index (PI), grain size, and constant volume swelling pressure tests were performed on unreinforced and reinforced expansive soil, to investigate the effects of polypropylene fiber and silica fume on the engineering properties of expansive clayey soil. The experimental results illustrate that the inclusion of polypropylene fiber has a significant effect on the upward swelling pressure and expansion property of expansive soil. The reduction in the upward swelling pressure and expansion is a function of fiber content. These results also indicated that the use of silica fume caused a reduction in upward swelling potential, and its effect was considerably more than the influence of fiber.

Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall−rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010−2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system.