Dr. Yakshansh Kumar, a PhD researcher at Delhi Technological University (DTU) specializing in pavement-soil dynamics and geotechnical engineering. He is recognized for research involving piezo sensors, finite element analysis, and published work in high-impact journals.
Key Details
Research Focus: Pavement-soil dynamics, geotechnical engineering, dynamic response analysis, and piezo-dynamics of geomaterials.
Education: Pursuing a PhD at Delhi Technological University (formerly Delhi College of Engineering).
Publications: He has published research in journals like the International Journal of Non-Linear Mechanics and the Journal of Vibration Engineering and Technologies.
Recognitions: Winner of Commendable Research Award and multiple awards for best technical papers at conferences.
Experience: Collaborated on research projects and served as a reviewer for Transportation Infrastructure Geotechnology.
Application of machine learning technique for dynamic analysis of confined geomaterial subjected to vibratory load Ammu Boban, Preeti Pateriya, Yakshansh Kumar, Kshitij Gaur, Ashutosh Trivedi AI in Civil Engineering, 2024 Computer programming-based numerical programs are firmly established in geotechnical engineering, with rapid growth of finite element modeling and machine learning techniques gaining much attention both in practice and academia. This study is intended to expedite the dissemination of advanced computer applications in terms of finite element simulation and machine learning models by investigating the dynamic response of geomaterials subjected to vibratory loads. Several trial models were built to perform the experimental investigations with a vibratory shaker, signal generator, several accelerometers, a data collection system, and other ancillary devices. The implicit integration techniques in commercialized software were adopted for numerical simulations. After data collection from numerical simulation, models were chosen, trained, and assessed to produce predictions that were then used in this study. Several technologies, including the ensemble boosted tree, squared exponential Gaussian Process Regression (GPR), Matern 5/2 GPR, exponential GPR, and decision tree architectures (fine and medium), were used to forecast the displacement of confined geomaterial. The displacement-depth ratio was found rising to 80% in the frequency range of 5 to 25 Hz, suggesting a considerable change in the behavior of the geomaterial. The Matern 5/2 GPR model showed better accuracy with an R2 value of 0.99, indicating an outstanding predictive ability. The Matern 5/2 GPR and boosted tree models could help better understand the links between displacement and its distribution along the direction of load application. The outcomes of this study based on computer-aided finite element programs can be effectively implemented in machine learning to develop computer programs. In conclusion, the computational machine learning models adopted in this study offer a new insight for uncovering hidden intrinsic laws and creating new knowledge for geotechnical researchers and practitioners.
Relative Dilation-Based Rock Mass Classification from Hardening and Softening Parameters in Laboratory and In-Situ Testing A Trivedi, Y Kumar Indian Geotechnical Journal, 1-14 , 2026 2026
Role of Molybdenum Disulphide and Film Thickness on the Piezoelectric Response of Polyvinylidene Fluoride Patches Subjected to Dynamic Loading in Confined Geomaterial Y Kumar, A Trivedi, SK Shukla National Academy Science Letters, 1-7 , 2026 2026
Impact of moving load vibrations on pavement damage supported by flow-controlled geomaterials Y Kumar, A Trivedi, SK Shukla International Journal of Non-Linear Mechanics 172, 105045 , 2025 2025 Citations: 2
Application of machine learning technique for dynamic analysis of confined geomaterial subjected to vibratory load A Boban, P Pateriya, Y Kumar, K Gaur, A Trivedi AI in Civil Engineering 3 (1), 2 , 2024 2024 Citations: 3
Investigating the Influence of Frequency on Piezo-dynamics of Polyvinylidene Fluoride (PVDF) Films Embedded in Confined Geomaterials Y Kumar, A Trivedi, SK Shukla Journal of Vibration Engineering & Technologies 12 (7), 8867-8886 , 2024 2024 Citations: 6
Damage evaluation in pavement-geomaterial system using finite element-scaled accelerated pavement testing Y Kumar, A Trivedi, SK Shukla Transportation Infrastructure Geotechnology 11 (3), 922-933 , 2024 2024 Citations: 10
Velocity-Induced Post-Elastic Flow Response of Pavement: A Finite Element-Based Statistical Investigation Y Kumar, A Trivedi, SK Shukla International Conference on Sustainable Infrastructure: Innovation … , 2024 2024
Deflections governed by the cyclic strength of rigid pavement subjected to structural vibration due to high-velocity moving loads Y Kumar, A Trivedi, SK Shukla Journal of Vibration Engineering & Technologies 12 (3), 3543-3562 , 2024 2024 Citations: 9
Influence of jute reinforcement on the stiffness capacity of cohesionless pavement geomaterials P Kumar, Y Kumar, A Trivedi International Conference on Interdisciplinary Approaches in Civil … , 2023 2023 Citations: 2
Numerical and Experimental Investigation of a Confined Geomaterial Subjected to Vibratory Load A Boban, Y Kumar, A Trivedi International Conference on Sustainable Infrastructure: Innovation … , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Damage evaluation in pavement-geomaterial system using finite element-scaled accelerated pavement testing Y Kumar, A Trivedi, SK Shukla Transportation Infrastructure Geotechnology 11 (3), 922-933 , 2024 2024 Citations: 10
Deflections governed by the cyclic strength of rigid pavement subjected to structural vibration due to high-velocity moving loads Y Kumar, A Trivedi, SK Shukla Journal of Vibration Engineering & Technologies 12 (3), 3543-3562 , 2024 2024 Citations: 9
Investigating the Influence of Frequency on Piezo-dynamics of Polyvinylidene Fluoride (PVDF) Films Embedded in Confined Geomaterials Y Kumar, A Trivedi, SK Shukla Journal of Vibration Engineering & Technologies 12 (7), 8867-8886 , 2024 2024 Citations: 6
Application of machine learning technique for dynamic analysis of confined geomaterial subjected to vibratory load A Boban, P Pateriya, Y Kumar, K Gaur, A Trivedi AI in Civil Engineering 3 (1), 2 , 2024 2024 Citations: 3
Impact of moving load vibrations on pavement damage supported by flow-controlled geomaterials Y Kumar, A Trivedi, SK Shukla International Journal of Non-Linear Mechanics 172, 105045 , 2025 2025 Citations: 2
Influence of jute reinforcement on the stiffness capacity of cohesionless pavement geomaterials P Kumar, Y Kumar, A Trivedi International Conference on Interdisciplinary Approaches in Civil … , 2023 2023 Citations: 2
Relative Dilation-Based Rock Mass Classification from Hardening and Softening Parameters in Laboratory and In-Situ Testing A Trivedi, Y Kumar Indian Geotechnical Journal, 1-14 , 2026 2026
Role of Molybdenum Disulphide and Film Thickness on the Piezoelectric Response of Polyvinylidene Fluoride Patches Subjected to Dynamic Loading in Confined Geomaterial Y Kumar, A Trivedi, SK Shukla National Academy Science Letters, 1-7 , 2026 2026
Velocity-Induced Post-Elastic Flow Response of Pavement: A Finite Element-Based Statistical Investigation Y Kumar, A Trivedi, SK Shukla International Conference on Sustainable Infrastructure: Innovation … , 2024 2024
Numerical and Experimental Investigation of a Confined Geomaterial Subjected to Vibratory Load A Boban, Y Kumar, A Trivedi International Conference on Sustainable Infrastructure: Innovation … , 2023 2023
