UJWAL M S

@assistant professor

Assistant Professor Civil Engineering
DAYANANDASAGAR COLLEGE OF ENGINEERING

UJWAL M S


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https://researchid.co/ujwalms

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering, Civil and Structural Engineering
29

Scopus Publications

221

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
    H. N. Sridhar, G. Shiva Kumar, H. K. Ramaraju, M. S. Ujwal, A. Vinay, Poornachandra Pandit
    Discover Sustainability, 2026
    Industrial waste materials are increasingly used in geotechnical engineering as partial replacements for cement, offering cost-effective and environmentally sustainable alternatives. This study investigates the California Bearing Ratio (CBR) and unconfined compressive strength (UCS) of lateritic soil stabilized with red mud (RM), copper slag (CS), and iron ore tailings (IOT) in proportions of 5–45%. A systematic laboratory program generated 155 experimental datasets, which were further used to develop predictive models with machine learning algorithms including K-Nearest Neighbours (KNN), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Multi-Layer Perceptron (MLP). Statistical indices the coefficient of determination (R 2 ), root mean square error (RMSE), and mean absolute error (MAE) along with Taylor diagrams and Regression Error Characteristic (REC) curves were applied for model evaluation. RFR and MLP achieved R 2 values above 0.90, showing superior performance. SHAP (SHapley Additive exPlanations) analysis highlighted curing period, maximum dry density (MDD), and CS dosage as the most influential features. Results confirmed that 30% CS significantly enhances both UCS and CBR, demonstrating its potential as a supplementary stabilizer. The study contributes a robust experimental machine learning framework that not only predicts UCS and CBR with high accuracy but also provides mechanistic insights, supporting circular economy practices and low-carbon pavement design.
  • Integration of performance testing and machine learning models for controlled low-strength material containing plastic waste aggregates
    M. Karthik, R. Anusha, M. S. Ujwal, G. Shiva Kumar, H. N. Sridhar, Poornachandra Pandit
    Discover Sustainability, 2026
    Global cement production reached approximately 4.1 billion metric tons in 2023, while global plastic production climbed to 413.8 million metric tons, projected to reach 590 million metric tons by 2050. This growth underscores the need for sustainable construction materials that can simultaneously divert plastic waste from landfills and reduce demand for virgin aggregates. Controlled Low-Strength Material (CLSM), or flowable fill, offers a promising platform by incorporating large volumes of industrial by-products. This study investigates the use of Recycled Plastic Coarse Aggregates (RPCA) produced from High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), PP (Polypropylene) and mixed plastic waste via a semi-mechanized process as a full replacement for natural coarse aggregates in CLSM. Low-, medium- and high-strength mixes were prepared with Portland cement, fly ash, M-sand and pond ash, and their fresh, hardened and in-service properties were evaluated. Results showed that RPCA-based CLSM achieved flow values of 590–620 mm, wet density reductions of ~ 30%, compressive strength up to 13.4 MPa at 28 days span both excavatable (≤ 8.3 MPa) and structural fill (> 8.3 MPa) CLSM categories as per ASTM D6103, with all low- and medium-strength mixes meeting typical excavatability requirements, shrinkage as low as 0.032%, permeability reductions of 9–15%, and thermal conductivity reductions of ~ 90% leading to a 92% increase in thermal resistivity relative to natural aggregate mixes. An integrated machine learning approach was employed to predict compressive strength from 252 experimental data points using Decision Tree, Random Forest and XG Boost regressors. XG Boost achieved the best performance with R 2 =0.97, MSE 0.08 and MAE = 0.12, outperforming the other models. SHAP analysis revealed that curing age and pond ash content were the most influential variables, followed by fine aggregate and RPCA proportion. This combined experimental–computational framework demonstrates that RPCA-based CLSM can deliver measurable environmental and performance gains while enabling data-driven mix optimisation for sustainable infrastructure applications. Overall, the proposed RPCA-based CLSM aligns with the United Nations Sustainable Development Goals by promoting responsible consumption and production (SDG 12), fostering industry innovation and resilient infrastructure (SDG 9), supporting sustainable cities and communities (SDG 11), and contributing to climate action through material efficiency and reduced embodied energy (SDG 13).
  • A data-driven framework for predicting CDI and TDI from mix design parameters using interpretable machine learning
    G. Shiva Kumar, N.C. Sanjay Shekar, M.S. Ujwal, M. Karthik, S. Sunil, Poornachandra Pandit
    Transportation Engineering, 2026
    • A machine learning framework was developed to predict CDI and TDI using asphalt mix design parameters. • The MLP model showed the highest accuracy for CDI prediction with an R² value of 0.92. • The Decision Tree model performed best for TDI prediction with the lowest RMSE and MAE. • SHAP analysis identified aggregate gradation as the most influential factor in densification behavior. Workability and long-term performance of asphalt mixtures are vital for durable pavements. Conventional evaluation of Compaction Densification Index (CDI) and Traffic Densification Index (TDI) using Superpave Gyratory Compactor (SGC) data is labour-intensive and fails to capture nonlinear relationships among mix variables. Although machine learning (ML) has been increasingly applied in pavement engineering, most studies address single indices and lack interpretability. This study bridges that gap by developing an explainable ML framework to simultaneously predict CDI and TDI from key mix parameters aggregate gradation, nominal maximum aggregate size (NMAS), and binder type. A dataset of 151 samples, compiled from published studies and laboratory databases, was divided into 70% training and 30% testing subsets. Three ML models Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) were developed and compared. The MLP achieved the highest predictive accuracy for CDI (R² = 0.92), while the Decision Tree slightly outperformed others for TDI (R² = 0.50). The study further aims to interpret model predictions using SHAP (SHapley Additive exPlanations) to identify the influence of individual input variables such as gradation and NMAS on compaction behaviour. The proposed interpretable framework enables rapid and reliable estimation of densification indices, offering a practical tool for optimizing asphalt mix designs, minimizing laboratory effort, and enhancing pavement quality and service life, thereby supporting Sustainable Development Goal 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production).
  • Experimental, statistical, and machine learning assessment of basalt–steel hybrid fiber reinforced concrete
    R Shanthi Vengadeshwari, N.C. Sanjay Shekar, G. Shiva Kumar, M.S. Ujwal, Raghavendra Mahesh, V.S. Annapoorna, Poornachandra Pandit
    Results in Materials, 2026
    This study presents a comprehensive investigation into the optimization of hybrid fiber reinforced concrete (HFRC) containing basalt and steel fibers using Response Surface Methodology (RSM) and predictive modeling through Machine Learning (ML). The experimental program employed a Central Composite Design (CCD) with basalt fiber (BF) and steel fiber (SF) volume fractions ranging from 0.25% to 0.75% to evaluate workability and mechanical properties, including compressive, split tensile, and flexural strengths, as well as elastic modulus. Statistical analysis revealed significant interactions between BF and SF, with R 2 values between 0.85 and 0.97 indicating excellent model fit. Multi-objective optimization achieved an optimal mix of 0.42% BF and 0.75% SF, yielding compressive strength of 52.58 MPa and split tensile strength of 6.10 MPa with a composite desirability of 0.68. Experimental validation confirmed the RSM model accuracy within a 7% error margin. Furthermore, machine learning models Decision Tree, Random Forest, and XGBoost were trained on 250 data points to predict compressive strength. The Random Forest Regressor demonstrated the best predictive performance with R 2 = 0.84, RMSE = 2.75 MPa, and MAE = 2.18 MPa. SHAP analysis identified the water-cement ratio, BF, and SF as the most influential parameters affecting compressive strength. The integrated RSM–ML framework offers an efficient approach for mix design optimization and property prediction of HFRC, supporting data-driven development of high-performance and sustainable concrete. This study directly contributes to UN Sustainable Development Goal (SDG) 9 (Industry, Innovation and Infrastructure) by promoting advanced material optimization and SDG 11 (Sustainable Cities and Communities) through the development of durable, resource-efficient concrete, while also aligning with SDG 12 (Responsible Consumption and Production) by enabling optimized material usage and reduced experimental waste.
  • Seismic vulnerability assessment of soft-story RC buildings on inclined terrain using machine learning models
    M. S. Ujwal, G. Shiva Kumar, M. G. Pranav., K. Sahana
    Asian Journal of Civil Engineering, 2026
  • Performance evaluation and machine learning-based prediction of black cotton soil stabilization with supplementary cementitious binders
    A. Vinay, H. N. Sridhar, G. Shiva Kumar, M. S. Ujwal, H. K. Ramaraju, Poornachandra Pandit
    Discover Applied Sciences, 2026
    Expansive black cotton (BC) soils pose significant challenges for pavement subgrades due to their high plasticity, low strength, and moisture sensitivity. This study investigates the stabilization of BC soil using cement, lime, fly ash, and ground granulated blast furnace slag (GGBS), combined with machine learning (ML) for predictive modeling of strength and bearing capacity. Laboratory experiments evaluated Atterberg limits, unconfined compressive strength (UCS), and California Bearing Ratio (CBR) across varying dosages and curing periods. The untreated soil exhibited poor performance (LL = 58%, PI = 31%, UCS ≈ 1.2 kg/cm², CBR ≈ 8.8%). Cement showed the greatest strength enhancement, with 8% cement achieving ~ 19 kg/cm² UCS and ~ 19% CBR after 28 days. Lime was most effective in improving subgrade performance, with 9% lime yielding ~ 7.7 kg/cm² UCS and the highest CBR of ~ 27–28%. GGBS at 30% provided ~ 8.9 kg/cm² UCS and ~ 9% CBR, while fly ash (40%) achieved only ~ 3 kg/cm² UCS and 6% CBR. To complement the experimental program, ML algorithms—Decision Tree, Random Forest, and XGBoost—were developed to predict UCS and CBR. Random Forest delivered the best accuracy for UCS (R² = 0.99, RMSE = 0.25, MAE = 0.13), while XGBoost excelled for CBR prediction (R² = 0.99, RMSE = 0.20, MAE = 0.11). SHAP analysis identified cement dosage and curing time as dominant factors for UCS, and lime as the most influential for CBR. The integration of laboratory data with ML models establishes a robust framework for optimizing stabilizer blends, reducing experimental effort, and promoting sustainable, low-carbon pavement design.
  • Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings
    M. S. Ujwal, N. C. Sanjay Shekar, Arya Prathap, C. B. Ranjan Gowda, N. Vibhashree, M. Karthik, G. Shiva Kumar
    Asian Journal of Civil Engineering, 2026
  • Sustainable concrete development using groundnut shell ash: A response surface methodology approach
    Raghavendra Mahesh, Shiva Kumar G, Ujwal M.S., Likheeth J., Vinay A., Poornachandra Pandit
    Cleaner Waste Systems, 2025
    Climate change and global warming are primarily driven by carbon dioxide (CO₂) emissions from fossil fuel combustion across energy, transportation, and industrial sectors. Among industrial contributors, the production of ordinary Portland cement (OPC) is a significant source of emissions due to its energy-intensive processes and chemical decomposition of limestone. Cement is widely used in concrete because of its cost-effectiveness and reliability; however, its high energy consumption and environmental impact necessitate the search for sustainable alternatives. Previous studies have explored various supplementary cementitious materials (SCMs), such as fly ash, silica fume, and groundnut shell ash (GSA), but many lack optimization in their application, particularly in tailoring the GSA content with water cement (w/c) ratios to achieve optimal workability and mechanical performance. Additionally, existing research often overlooks systematic experimental designs that comprehensively evaluate fresh and hardened properties via advanced statistical techniques. This study addresses these gaps by employing response surface methodology (RSM) to optimize concrete mixtures incorporating GSA as a partial cement replacement (3–12 %) with w/c ratios ranging from 0.4–0.6. Workability (slump, Vee–Bee consistency, compaction factor) and mechanical properties (compressive strength, flexural strength, and split tensile strength) were evaluated according to Indian standards. The results demonstrated that the GSA significantly influenced the concrete properties, with an optimal mixture of 6.27 % GSA and a 0.42 w/c ratio achieving a desirability of 66.38 %. This study provides a data-driven approach to enhancing concrete sustainability by utilizing agricultural waste while addressing key shortcomings in prior research. The optimized concrete mixture not only demonstrated enhanced mechanical and fresh properties but also significantly contributes to the broader agenda of sustainable construction. By incorporating groundnut shell ash (GSA), an agricultural waste, this study promotes circular economy practices and reduces reliance on traditional cement, which is a major contributor to global CO₂ emissions. The findings underscore the feasibility of replacing a portion of OPC with GSA without compromising performance, thereby supporting the transition to greener infrastructure materials. This research lays the groundwork for scalable, eco-efficient concrete production, especially in regions with abundant agricultural residues. • The study shows GSA can enhance concrete sustainability and address existing research gaps. • RSM optimized concrete mixes using 3–12 % GSA and w/c ratios between 0.4 and 0.6. • Workability and strength properties were evaluated as per Indian Standards. • The optimal mix was 6.27 % GSA and 0.42 w/c ratio, with a 66.38 % desirability score.
  • Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
    Chidananda M. Linganagoudar, G. Shiva Kumar, M. S. Ujwal, Varun S. Ullur, Poornachandra Pandit
    Scientific Reports, 2025
    The growing demand for sustainable infrastructure solutions has driven the exploration of alternative materials for soil stabilization, especially for problematic soils such as black cotton (BC) soil. Owing to its high shrink-swell behavior, BC soil poses significant challenges in construction and pavement applications. This study evaluated the potential use of cement (up to 9.24%), flue gas desulfurization (FGD) gypsum (up to 3.41%), and industrial byproducts from thermal power plants as stabilizing agents to enhance the geotechnical properties of BC soil. A central composite design under the framework of response surface methodology (RSM) was employed to optimize the mix proportions and assess the effects on the unconfined compressive strength (UCS), California bearing ratio (CBR), and plasticity index (PI). The findings demonstrated substantial improvements in soil strength and a significant reduction in plasticity. The optimum mixture of 9.24% cement and 3.41% FGD gypsum yielded a desirability score of 71%, indicating an effective balance between strength gain and workability. This study underscores the viability of using FGD gypsum as a sustainable and eco-friendly soil stabilizer, offering an economical and efficient method for improving subgrade performance in flexible pavement systems. The results contribute to advancing green construction practices by utilizing industrial waste in geotechnical applications.
  • Correction: Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability (Low-carbon Materials and Green Construction, (2024), 2, 1, (29), 10.1007/s44242-024-00054-6)
    Y H Sudeep, M S Ujwal, Raghavendra Mahesh, G. Shiva Kumar, A Vinay, H. K. Ramaraju
    Low Carbon Materials and Green Construction, 2025
  • Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models
    R. Shanthi Vengadeshwari, M. S. Ujwal, N. C. Sanjay Shekar, T. N. Akash, K. Sahana, G. Shiva Kumar
    Asian Journal of Civil Engineering, 2025
  • Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
    H.N. Raghavendra, M.S. Ujwal, G. Shiva Kumar, T.P. Sanjeev, H.S. Raghavendra Prajwal, B.V. Ajith, Poornachandra Pandit
    Cleaner Waste Systems, 2025
  • SHAP-based prediction and optimization of compressive strength in M30 concrete with dry sewage sludge as fine aggregate replacement
    R. Shanthi Vengadeshwari, M. S. Ujwal, G. Shiva Kumar, Raghavendra Mahesh, N. Sanjay, K. N. Rajiv, Poornachandra Pandit
    Discover Materials, 2025
  • Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
    Shiva Kumar G, Raghavendra Mahesh, Ujwal M.S., Poornachandra Pandit, Rajiv K.N., Chandan A., H.K. Ramaraju
    Case Studies in Construction Materials, 2025
  • Toward sustainable self-compacting concrete: Rheological, mechanical, durability, and microstructural evaluation of biomaterial-based cement substituents
    Ujwal M S, Shiva Kumar G, Pramod S H, Sridhar H N, Poornachandra Pandit
    Results in Engineering, 2025
  • Laboratory performance of sustainable stone matrix asphalt mixtures utilizing electric arc furnace slag and waste plastic
    Shiva Kumar Govindaraju, Nitin Gopanahally Chandrasekharaiah, Gurudeep Ganesh, Sunil Siddaraju, Ujwal Mallaiah Sudhamani, Hanumanahally Kambada Ramaraju
    Journal of Road Engineering, 2025
  • State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
    G. Shiva Kumar, G. C. Nitin, G. Gurudeep, M. S. Ujwal, H. K. Ramaraju
    Journal of Building Pathology and Rehabilitation, 2025
  • Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
    Chidananda M Linganagoudar, Shiva Kumar G, M S Ujwal, G Rohith, A Vinay, Poornachandra Pandit
    Materials Research Express, 2025
  • Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
    M. S. Ujwal, G. Shiva Kumar, Raghavendra Mahesh, H. K. Ramaraju
    Multiscale and Multidisciplinary Modeling Experiments and Design, 2025
  • Evaluating the impact of spent coffee grounds on concrete's workability and mechanical properties using response surface methodology
    G. Shiva Kumar, M. S. Ujwal, H. Anil Kumar, Y. H. Sudeep, G. Venkatesha
    Innovative Infrastructure Solutions, 2025
  • Prediction of moisture damage properties of asphalt mixtures using machine learning models
    Shiva Kumar G, Nitin G C, Gurudeep G, Ujwal M S, Ramaraju H K
    Journal of Structural Integrity and Maintenance, 2025
  • A step towards sustainability to optimize the performance of self-compacting concrete by incorporating fish scale powder: A response surface methodology approach
    M. S Ujwal, G. Shiva Kumar
    Emergent Materials, 2024
  • Evaluating the impact of V-shaped columns on the dynamic behavior of RC buildings on sloped ground
    Y. H. Sudeep, M. S. Ujwal, K. R. Purushotham, R. Shanthi Vangadeshwari, G. Shiva Kumar
    Asian Journal of Civil Engineering, 2024
  • Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability
    Y H Sudeep, M S Ujwal, Raghavendra Mahesh, G. Shiva Kumar, A Vinay, H. K. Ramaraju
    Low Carbon Materials and Green Construction, 2024
  • Optimizing the properties of seashell ash powder based concrete using Response Surface Methodology
    M. S. Ujwal, A. N. Rudresh, Thummala Pavan Sathya, G. Shiva Kumar, A. Vinay, H. N. Sridhar, H. K. Ramaraju
    Asian Journal of Civil Engineering, 2024
  • Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning
    S. Sathvik, Rakesh Kumar, Nestor Ulloa, Pshtiwan Shakor, M. S. Ujwal, Kennedy Onyelowe, G. Shiva Kumar, Mary Subaja Christo
    Scientific Reports, 2024
  • Comparative study of step-back and step-back setback configurations of multi-story buildings with varying height on sloped terrain
    Sudeep Y. H., Ujwal M. S., Sridhar H. N., Sathvik S., G. Shiva Kumar, H. K. Ramaraju
    Asian Journal of Civil Engineering, 2024
  • Effect of soft story conditions on the seismic performance of tall concrete structures
    M. S. Ujwal, G. Shiva Kumar, S. Sathvik, H. K. Ramaraju
    Asian Journal of Civil Engineering, 2024
  • FEASIBILITY OF USING EGG SHELL POWDER AS SUPPLEMENTARY CEMENTITIOUS MATERIAL IN SELF COMPACTING CONCRETE
    Indian Concrete Journal, 2023

RECENT SCHOLAR PUBLICATIONS

  • A review of eggshell and fish scale powders as sustainable supplementary cementitious materials for concrete
    MS Ujwal, G Shiva Kumar
    Discover Concrete and Cement 2 (1), 23 , 2026
    2026
  • Experimental, Statistical, and Machine Learning Assessment of Basalt–Steel Hybrid Fiber Reinforced Concrete
    RS Vengadeshwari, NCS Shekar, GS Kumar, MS Ujwal, R Mahesh, ...
    Results in Materials, 100953 , 2026
    2026
  • A Data-Driven Framework for Predicting CDI and TDI from Mix Design Parameters using Interpretable Machine Learning
    GS Kumar, NCS Shekar, MS Ujwal, M Karthik, S Sunil, P Pandit
    Transportation Engineering, 100437 , 2026
    2026
  • Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings
    MS Ujwal, NC Sanjay Shekar, A Prathap, CB Ranjan Gowda, ...
    Asian Journal of Civil Engineering, 1-25 , 2026
    2026
  • Seismic vulnerability assessment of soft-story RC buildings on inclined terrain using machine learning models
    MS Ujwal, G Shiva Kumar, K Sahana
    Asian Journal of Civil Engineering 27 (4), 1601-1625 , 2026
    2026
  • Integration of performance testing and machine learning models for controlled low-strength material containing plastic waste aggregates
    M Karthik, R Anusha, MS Ujwal, GS Kumar, HN Sridhar, P Pandit
    Discover Sustainability , 2026
    2026
  • Performance evaluation and machine learning-based prediction of black cotton soil stabilization with supplementary cementitious binders
    A Vinay, HN Sridhar, G Shiva Kumar, MS Ujwal, HK Ramaraju, P Pandit
    Discover Applied Sciences , 2026
    2026
  • Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
    HN Sridhar, G Shiva Kumar, HK Ramaraju, MS Ujwal, A Vinay, P Pandit
    Discover Sustainability , 2025
    2025
    Citations: 4
  • Sustainable concrete development using groundnut shell ash: A response surface methodology approach
    R Mahesh, S Kumar, P Pandit
    Cleaner Waste Systems 12, 100379 , 2025
    2025
    Citations: 4
  • Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models
    R Shanthi Vengadeshwari, MS Ujwal, NC Sanjay Shekar, TN Akash, ...
    Asian Journal of Civil Engineering 26 (12), 4981-5001 , 2025
    2025
    Citations: 3
  • SHAP-based prediction and optimization of compressive strength in M30 concrete with dry sewage sludge as fine aggregate replacement
    R Shanthi Vengadeshwari, MS Ujwal, G Shiva Kumar, R Mahesh, ...
    Discover Materials 5 (1), 183 , 2025
    2025
    Citations: 3
  • Laboratory Performance of Sustainable Stone Matrix Asphalt Mixtures Utilizing Electric Arc Furnace Slag and Waste Plastic
    HKR G Shiva Kumar, GC Nitin, G Gurudeep, S Sunil, MS Ujwal
    Journal of Road Engineering 5 (3), 343-352 , 2025
    2025
    Citations: 4
  • Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
    GS Kumar, R Mahesh, MS Ujwal, P Pandit, KN Rajiv, A Chandan, ...
    Case Studies in Construction Materials, e05113 , 2025
    2025
    Citations: 5
  • Toward Sustainable Self-Compacting Concrete: Rheological, Mechanical, Durability, and Microstructural Evaluation of Biomaterial-Based Cement Substituents
    MS Ujwal, S Kumar, SH Pramod, HN Sridhar, P Pandit
    Results in Engineering, 106504 , 2025
    2025
    Citations: 1
  • Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
    HN Raghavendra, MS Ujwal, GS Kumar, TP Sanjeev, HSR Prajwal, ...
    Cleaner Waste Systems, 100360 , 2025
    2025
    Citations: 6
  • Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
    CM Linganagoudar, GS Kumar, MS Ujwal, VS Ullur, P Pandit
    Scientific Reports 15 (1), 23580 , 2025
    2025
    Citations: 8
  • Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
    CM Linganagoudar, SK G, MS Ujwal, G Rohith, A Vinay, P Pandit
    Materials Research Express 12 (6), 065501 , 2025
    2025
    Citations: 8
  • State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
    GS Kumar, GC Nitin, G Gurudeep, MS Ujwal, HK Ramaraju
    Journal of Building Pathology and Rehabilitation 10 (1), 51 , 2025
    2025
    Citations: 4
  • Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
    MS Ujwal, GS Kumar, R Mahesh, HK Ramaraju
    Multiscale and Multidisciplinary Modeling, Experiments and Design 8 (4), 207 , 2025
    2025
    Citations: 18
  • Prediction of moisture damage properties of asphalt mixtures using machine learning models
    HKR G Shiva Kumar, GC Nitin, G Gurudeep, MS Ujwal
    Journal of Structural Integrity and Maintenance 10 (2) , 2025
    2025
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning
    S Sathvik, R Kumar, N Ulloa, P Shakor, MS Ujwal, K Onyelowe, GS Kumar, ...
    Scientific Reports 14 (1), 11552 , 2024
    2024
    Citations: 34
  • Effect of soft story conditions on the seismic performance of tall concrete structures
    MS Ujwal, GS Kumar, S Sathvik, HK Ramaraju
    Asian journal of civil engineering 25 (4), 3141-3149 , 2024
    2024
    Citations: 32
  • Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability
    YH Sudeep, MS Ujwal, R Mahesh, G Shiva Kumar, A Vinay, HK Ramaraju
    Low-carbon Materials and Green Construction 2 (1), 29 , 2024
    2024
    Citations: 19
  • Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
    MS Ujwal, GS Kumar, R Mahesh, HK Ramaraju
    Multiscale and Multidisciplinary Modeling, Experiments and Design 8 (4), 207 , 2025
    2025
    Citations: 18
  • Optimizing the properties of seashell ash powder based concrete using response surface methodology
    MS Ujwal, AN Rudresh, TP Sathya, G Shiva Kumar, A Vinay, HN Sridhar, ...
    Asian Journal of Civil Engineering 25 (8), 6021-6036 , 2024
    2024
    Citations: 17
  • A step towards sustainability to optimize the performance of self-compacting concrete by incorporating fish scale powder: A response surface methodology approach
    MS Ujwal, GS Kumar
    Emergent Materials 7 (6), 3121-3142 , 2024
    2024
    Citations: 13
  • FEASIBILITY OF USING EGG SHELL POWDER AS SUPPLEMENTARY CEMENTITIOUS MATERIAL IN SELF COMPACTING CONCRETE
    KVR Ujwal, M.S., Ganesh, B., Darshan, M., Jyothi, T.K., Nagendra, R., Jois
    Indian Concrete Journal 97 (12), 37-47 , 2023
    2023
    Citations: 13
  • Comparative study of step-back and step-back setback configurations of multi-story buildings with varying height on sloped terrain
    S YH, U MS, S HN, S S, GS Kumar, HK Ramaraju
    Asian Journal of Civil Engineering 25 (7), 5067-5088 , 2024
    2024
    Citations: 11
  • Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
    CM Linganagoudar, GS Kumar, MS Ujwal, VS Ullur, P Pandit
    Scientific Reports 15 (1), 23580 , 2025
    2025
    Citations: 8
  • Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
    CM Linganagoudar, SK G, MS Ujwal, G Rohith, A Vinay, P Pandit
    Materials Research Express 12 (6), 065501 , 2025
    2025
    Citations: 8
  • Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
    HN Raghavendra, MS Ujwal, GS Kumar, TP Sanjeev, HSR Prajwal, ...
    Cleaner Waste Systems, 100360 , 2025
    2025
    Citations: 6
  • Evaluating the impact of spent coffee grounds on concrete's workability and mechanical properties using response surface methodology
    GS Kumar, MS Ujwal, HA Kumar, YH Sudeep, G Venkatesha
    Innovative Infrastructure Solutions 10 (2), 70 , 2025
    2025
    Citations: 6
  • Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
    GS Kumar, R Mahesh, MS Ujwal, P Pandit, KN Rajiv, A Chandan, ...
    Case Studies in Construction Materials, e05113 , 2025
    2025
    Citations: 5
  • Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
    HN Sridhar, G Shiva Kumar, HK Ramaraju, MS Ujwal, A Vinay, P Pandit
    Discover Sustainability , 2025
    2025
    Citations: 4
  • Sustainable concrete development using groundnut shell ash: A response surface methodology approach
    R Mahesh, S Kumar, P Pandit
    Cleaner Waste Systems 12, 100379 , 2025
    2025
    Citations: 4
  • Laboratory Performance of Sustainable Stone Matrix Asphalt Mixtures Utilizing Electric Arc Furnace Slag and Waste Plastic
    HKR G Shiva Kumar, GC Nitin, G Gurudeep, S Sunil, MS Ujwal
    Journal of Road Engineering 5 (3), 343-352 , 2025
    2025
    Citations: 4
  • State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
    GS Kumar, GC Nitin, G Gurudeep, MS Ujwal, HK Ramaraju
    Journal of Building Pathology and Rehabilitation 10 (1), 51 , 2025
    2025
    Citations: 4
  • Prediction of moisture damage properties of asphalt mixtures using machine learning models
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