Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Urs C, Yogeshraj; Prasanna, H S; Unnam, Anil Title A name given to the resource A physics-informed neural network framework for consolidation parameter prediction using controlled clay-sand mixtures Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Results in Engineering;Volume;29;Issue;;Article No.;109255; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.rineng.2026.109255" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.rineng.2026.109255</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029067932?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029067932?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Urs C Y., Department of Civil Engineering, The National Institute of Engineering, Mysuru, Visvesvaraya Technological University, Karnataka, Belgaum, India, Department of Civil Engineering, SOET, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Prasanna H.S., Department of Civil Engineering, The National Institute of Engineering, Mysuru, Visvesvaraya Technological University, Karnataka, Belgaum, India; Unnam A., Department of Civil Engineering, The National Institute of Engineering, Mysuru, Visvesvaraya Technological University, Karnataka, Belgaum, India Description An account of the resource This paper introduces a novel Physics-Informed Neural Network (PINN) model for predicting the coefficient of consolidation (Cv) in high plasticity clays. The model was trained from experimental data obtained from controlled clay-sand mixtures. The input parameters include clay content, Atterberg limits, initial void ratio, compaction energy, applied pressure and consolidation characteristics like compression index (Cc) and volumetric compressibility (mv). Additional parameters like plasticity index, porosity, activity-clay interaction and compaction efficiency were derived from feature engineering. The proposed PINN model combines data-driven loss and physics-based loss into a total loss function. The physics loss includes three constraints derived from modified Kozeny-Carman equations, activity-based mineralogical relations, and compression-volume consistency. Hyperparameter optimization identified the optimal configuration: 800 epochs, learning rate 0.001, architecture [128, 64, 32], and physics loss weights distributed as 0.7, 0.25, and 0.05. Five-fold cross-validation demonstrated robust performance (R2 = 0.9903 0.0026), significantly outperforming baseline neural networks (R2 = 0.9682 0.0126, p = 0.0116) with 73.9% reduction in Root Mean Square Error (RMSE = 6.37 10-11 m/s) and 5.71% improvement in Mean Absolute Percentage Error (MAPE = 4.48%). External validation showed the PINN (R = 0.9968) substantially outperformed empirical correlations (best R2 = 0.1636) and conventional machine learning models (best R2 = 0.9878). SHapley Additive exPlanations (SHAP) interpretability analysis validated physically meaningful decision-making, with plastic limit and activity emerging as primary drivers. This framework provides a transferable, physics-consistent solution applicable across diverse clay types for foundation design and site characterization. Copyright 2026. Published by Elsevier B.V. Subject The topic of the resource Activity-clay interaction; Coefficient of consolidation; Explainable AI; Geotechnical engineering; High plasticity clays; Machine learning; Physics-informed neural networks Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 25901230; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online