Dr Vinay A

@dayanandasagar.edu

Assistant Professor and Department of Civil Engineering
Dayananda Sagar College of Engineering

Dr Vinay A


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https://researchid.co/vinayangadi

EDUCATION

M.Tech, Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering, Urban Studies, Transportation, Artificial Intelligence
7

Scopus Publications

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.
  • 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.
  • 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
  • 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
    This study investigates the stabilization of lateritic soil through partial replacement of cement with flue gas desulfurization (FGD) gypsum, aiming to enhance its engineering properties for pavement subgrade applications. Lateritic soils are known for their high plasticity and low strength, which limit their utility in infrastructure. To address these challenges, soil specimens were treated with varying cement contents (3%, 6%, 9%) and FGD gypsum additions (1%–6%). Laboratory tests were conducted to evaluate plasticity, compaction, permeability, unconfined compressive strength (UCS), California Bearing Ratio (CBR), and fatigue behaviour. The optimal mix 6% cement with 3% FGD gypsum demonstrated significant improvements: UCS increased by over 110% after 28 days, permeability reduced by 26%, and soaked CBR improved by 56% compared to untreated soil. Additionally, fatigue life showed remarkable enhancement under cyclic loading, indicating increased durability for high-traffic applications. To support predictive insights, machine learning models including Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) were trained on 168 data samples. The MLP and Random Forest models achieved high prediction accuracy (R2 ≈ 0.98), effectively capturing the non-linear interactions between mix proportions and UCS. SHAP (SHapley Additive exPlanations) analysis identified curing duration as the most influential factor affecting strength development. This integrated experimental–computational approach not only validates the feasibility of using FGD gypsum in sustainable soil stabilization but also demonstrates the effectiveness of machine learning in predicting key geotechnical parameters, reducing reliance on extensive laboratory testing and promoting data-driven pavement design.
  • 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
    This research explores the utilization of wheat straw ash (WSA), an agricultural by-product enriched with amorphous silica, as a partial cement replacement in concrete production. The WSA content ranged from 4% to 16% by mass, with water-to-cement (w/c) ratios varying between 0.4 and 0.6. Using response surface methodology (RSM) combined with central composite design, this study optimized mix designs and developed predictive models for key performance indicators, including workability and mechanical properties of concrete. The results demonstrate that an optimal balance of the WSA and a reduced w/c ratio significantly enhance both the workability and mechanical performance of concrete. The pozzolanic reaction between WSA and calcium hydroxide promotes the formation of calcium silicate hydrate (C-S-H) gel. The optimal mix composition, comprising 10.12% w(WSA) with a w/c ratio of 0.45, achieved a desirability score of 71.83%. This ground-breaking research underscores the viability of WSA as a supplementary cementitious material, offering a sustainable solution for concrete production while simultaneously enhancing its workability and mechanical properties.
  • 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
  • Performance of Stone Matrix Asphalt Modified with Crumb Rubber and Fibres
    and G. Shiva Kumar
    Jordan Journal of Civil Engineering, 2023
    This article investigates the impact of Crumb Rubber Modified Bitumen (CRMB) and fibre additives (aramid fibre and basalt fibre) on the performance properties of Stone Matrix Asphalt (SMA) mixtures. Tests were conducted to evaluate mix design, draindown, cantabro loss, moisture sensitivity, rutting resistance and fatigue behavior. The Marshall method, the draindown parameters (ASTM D6390-11) and the cantabro loss characteristics (ASTM D 7064) were used to examine the mix design qualities. The modified Lottman test was used to assess the moisture sensitivity of SMA mixes. The roller compactor cum rut analyzer was used to assess rutting resistance. Findings showed that CRMB and fibre additives effectively controlled binder draindown and minimized abrasion loss in SMA mixtures. SMA-CRMB mixtures had higher draindown, but comparable cantabro loss than SMA-AF and SMA-BF mixtures. Incorporating CRMB and fibre additives enhanced moisture sensitivity, rutting resistance and fatigue behavior. SMA mixtures with 0.3% fibre addition displayed similar performance properties to SMA with CRMB. Further, substituting fibre additions for CRMB in SMA combinations may yield similar performance. KEYWORDS: Stone matrix asphalt, Crumb rubber-modified bitumen, Fibre additives, Draindown, Cantabro loss, Moisture sensitivity, Rutting resistance, Fatigue behaviour.