Abhishek Sharma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Abhishek Sharma has a total of 83 co-authors. Here are the top co-authors by number of shared publications:
- Kanwarpreet Singh
Department of Civil Engineering, Bahra Research Innovation & Knowledge Cluster (BRIKC), Rayat Bahra University, Greater Mohali 140103, Punjab, India
21 shared publications - Aditya Kumar Tiwary
Department of Civil Engineering, University Centre for Research and Development, Chandigarh University, Mohali, India
7 shared publications - Amenjor Senagah
Department of Civil Engineering, College of Engineering, University of Liberia, Monrovia, Liberia
4 shared publications - Ravi Kumar Sharma
Civil Engineering Department, National Institute of Technology, Hamirpur, India
4 shared publications - Harpreet Singh
Department of Civil Engineering, University Institute of Engineering, Chandigarh University, Mohali, India
3 shared publications Kanwarpreet Singh
University Centre for Research and Development, Civil Engineering Department, Chandigarh University, Punjab, India
3 shared publications
Latest Publications
- 1 December 2026
- Published by American Society of Civil Engineers (ASCE) in Journal of Hydrologic Engineering
- Vol. 31 (6) , 04026061
An Integrated Hydro-Geotechnical Framework for Landslide Susceptibility Zonation and Slope Stability Assessment: Application to the Pithoragarh Region, Uttarakhand, India
Landslides are among the most significant natural hazards in the Himalayan region of India, causing substantial damage to infrastructure, natural resources, and local communities. This study presents an integrated hydro-geotechnical approach for regional landslide susceptibility assessment by combining statistical susceptibility mapping with slope stability evaluation. The proposed framework considers the influence of key hydrologic factors, including rainfall, drainage density, topographic wetness, and groundwater conditions, together with geotechnical properties to better explain the mechanisms controlling slope failure. Five bivariate statistical models, namely, frequency ratio (FR), information value (IV), weight of evidence (WoE), certainty factor (CF), and Shannon entropy (SE), were applied to produce landslide susceptibility maps. Their performance was evaluated using the area under the curve (AUC) and landslide density index (LDI). Among the evaluated models, SE achieved the highest predictive accuracy ( AUC = 87.17 % ), followed closely by FR (87.06%). The results indicate that steep slopes ( > 30 ° ), weak lithological formations, high drainage density, and groundwater conditions are the main factors controlling landslide occurrence in the study area. Slope stability was further evaluated using laboratory-derived geotechnical parameters and circular failure charts (CFC), providing an independent assessment of slope conditions under different groundwater scenarios. The combined use of statistical modeling, hydrologic factors, and geotechnical verification improves the reliability of landslide susceptibility assessment and provides a practical framework that can be applied in other mountainous regions affected by rainfall-induced landslides.
- 1 December 2026
- Published by American Society of Civil Engineers (ASCE) in Journal of Hydrologic Engineering
- Vol. 31 (6) , 04026061
An Integrated Hydro-Geotechnical Framework for Landslide Susceptibility Zonation and Slope Stability Assessment: Application to the Pithoragarh Region, Uttarakhand, India
Landslides are among the most significant natural hazards in the Himalayan region of India, causing substantial damage to infrastructure, natural resources, and local communities. This study presents an integrated hydro-geotechnical approach for regional landslide susceptibility assessment by combining statistical susceptibility mapping with slope stability evaluation. The proposed framework considers the influence of key hydrologic factors, including rainfall, drainage density, topographic wetness, and groundwater conditions, together with geotechnical properties to better explain the mechanisms controlling slope failure. Five bivariate statistical models, namely, frequency ratio (FR), information value (IV), weight of evidence (WoE), certainty factor (CF), and Shannon entropy (SE), were applied to produce landslide susceptibility maps. Their performance was evaluated using the area under the curve (AUC) and landslide density index (LDI). Among the evaluated models, SE achieved the highest predictive accuracy ( AUC = 87.17 % ), followed closely by FR (87.06%). The results indicate that steep slopes ( > 30 ° ), weak lithological formations, high drainage density, and groundwater conditions are the main factors controlling landslide occurrence in the study area. Slope stability was further evaluated using laboratory-derived geotechnical parameters and circular failure charts (CFC), providing an independent assessment of slope conditions under different groundwater scenarios. The combined use of statistical modeling, hydrologic factors, and geotechnical verification improves the reliability of landslide susceptibility assessment and provides a practical framework that can be applied in other mountainous regions affected by rainfall-induced landslides.
- Open Access
- 28 October 2026
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 9 (1) , 196
Synergistic stabilization of expansive clay using cement kiln dust, calcium carbide residue, and polypropylene fibers for pavement subgrades
- 28 October 2026
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 9 (1) , 196
Open AccessSynergistic stabilization of expansive clay using cement kiln dust, calcium carbide residue, and polypropylene fibers for pavement subgrades
- 24 October 2026
- Published by Springer Nature in Plant Cell Reports
- Vol. 45 (11) , 342
Heavy metal stress mitigation in plants by metal-resistant fungi: molecular and biotechnological insights
Heavy Metals (HMs) are persistent, non-biodegradable metallic elements that accumulate into soils due to weathering processes and anthropogenic activities, such as mining, smelting and industrial discharges. They can be classified as essential (Cu, Zn, Ni, Fe, Mn), which are toxic in excessive amounts, and non-essential (Cd, Pb, As, Hg), which are dangerous even in small amounts. HMs affect plant metabolism causing production of Reactive Oxygen Species (ROS), growth inhibition and nutrient deficiency, Arabidopsis thaliana seedlings germination was lowered to 55% and 21% at concentrations of 50 and 100 mg L⁻1 CdCl₂. Phytochelatins, metallothioneins and vacuole sequestration protect plants, but become ineffective at high concentrations of HMs. Metal-resistant fungi help to cope with stress conditions using mechanisms of biosorption, bioaccumulation, biomineralization, bioleaching and biotransformation, adapted Aspergillus niger managed to leach 78.5% Ni and 82.3% Mo during 30 days, while Penicillium chrysogenum eliminated 63% Cd in soil. Partners of fungi, such as Trichoderma and arbuscular mycorrhizal fungi, regulate production of ROS, activation of MAPKs and ABA/JA/SA/ethylene/auxin signaling pathway and expression of MT, PCS, GST, ABC, NRAMP and HMA genes under control of WRKY, MYB, NAC and bZIP transcription factors. Graphic abstract
- 24 October 2026
- Published by Springer Nature in Plant Cell Reports
- Vol. 45 (11) , 342
Heavy metal stress mitigation in plants by metal-resistant fungi: molecular and biotechnological insights
Heavy Metals (HMs) are persistent, non-biodegradable metallic elements that accumulate into soils due to weathering processes and anthropogenic activities, such as mining, smelting and industrial discharges. They can be classified as essential (Cu, Zn, Ni, Fe, Mn), which are toxic in excessive amounts, and non-essential (Cd, Pb, As, Hg), which are dangerous even in small amounts. HMs affect plant metabolism causing production of Reactive Oxygen Species (ROS), growth inhibition and nutrient deficiency, Arabidopsis thaliana seedlings germination was lowered to 55% and 21% at concentrations of 50 and 100 mg L⁻1 CdCl₂. Phytochelatins, metallothioneins and vacuole sequestration protect plants, but become ineffective at high concentrations of HMs. Metal-resistant fungi help to cope with stress conditions using mechanisms of biosorption, bioaccumulation, biomineralization, bioleaching and biotransformation, adapted Aspergillus niger managed to leach 78.5% Ni and 82.3% Mo during 30 days, while Penicillium chrysogenum eliminated 63% Cd in soil. Partners of fungi, such as Trichoderma and arbuscular mycorrhizal fungi, regulate production of ROS, activation of MAPKs and ABA/JA/SA/ethylene/auxin signaling pathway and expression of MT, PCS, GST, ABC, NRAMP and HMA genes under control of WRKY, MYB, NAC and bZIP transcription factors. Graphic abstract
- 22 October 2026
- Published by Springer Nature in Environment Systems and Decisions
- Vol. 46 (4) , 91
Green analytical strategies for monitoring atmospheric microplastics in mountain ecosystems: current advances and challenges
- 22 October 2026
- Published by Springer Nature in Environment Systems and Decisions
- Vol. 46 (4) , 91
Green analytical strategies for monitoring atmospheric microplastics in mountain ecosystems: current advances and challenges
- Open Access
- 17 June 2026
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 9 (1) , 150
Performance-based evaluation and UCS prediction of rice husk ash-stabilized expansive subgrade soil using experiments and explainable machine learning
- 17 June 2026
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 9 (1) , 150
Open AccessPerformance-based evaluation and UCS prediction of rice husk ash-stabilized expansive subgrade soil using experiments and explainable machine learning
- Open Access
- 22 April 2026
- Published by Springer Nature in Journal of Infrastructure Preservation and Resilience
- Vol. 7 (1)
Thermal resilience and empirical modeling for residual properties of concrete with silica fume and waste glass powder
- 22 April 2026
- Published by Springer Nature in Journal of Infrastructure Preservation and Resilience
- Vol. 7 (1)
Open AccessThermal resilience and empirical modeling for residual properties of concrete with silica fume and waste glass powder
- 29 October 2025
- Published by Springer Nature in Journal of Building Pathology and Rehabilitation
- Vol. 11 (1) , 1-22
Fire-resistant mortar with waste-derived additives: machine learning prediction of compressive strength at elevated temperatures
- 29 October 2025
- Published by Springer Nature in Journal of Building Pathology and Rehabilitation
- Vol. 11 (1) , 1-22
Fire-resistant mortar with waste-derived additives: machine learning prediction of compressive strength at elevated temperatures