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All Journal Journal of ICT, Design, Engineering and Technological Science
Saad Hanif
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Machine Learning–Based Prediction and Interpretability Analysis of Ultra‑High‑Performance Concrete Compressive Strength Using Random Forest Imran Ali Channa; Muhammad Khisrow Khan; Saad Hanif; Abdul Wahab
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.5

Abstract

Ultra‑High‑Performance Concrete (UHPC) is a considerably advanced cementitious concrete with great characteristics of strength and durability, but the compressive strength is highly dependent on the multi‑faceted interplay between mixture proportions and curing conditions. These interactions are nonlinear and multivariate, making it difficult to accurately estimate the UHPC compressive strength using the previous experimental and empirical methods. In the paper, a Random Forest (RF) regression model has been constructed to estimate UHPC compressive strength based on a large‑scale dataset of 810 samples and 13 predictors (material composition and curing parameters). Multiple statistical measures were strictly used to evaluate the performance of the model, such as R2, RMSE, MAE, MAPE, and CVRMSE, as well as 10‑fold cross‑validation to evaluate stability and ability to generalize. The optimized RF model had a high predictive accuracy with a value of 0.96 on the testing set and small values of errors, which showed high robustness and consistency in diverse segmentations of data. Hyperparameter tuning also improved the model performance by finding a balance between model complexity and generalization. SHAP (Shapley Additive Explanations) analysis was used to enhance the transparency and interpretability of the models, to measure the contribution of the individual input feature to the compressive strength predictions. The findings demonstrated that curing age, fibre, silica fume, and dosage of superplasticizer were the most significant parameters that controlled the strength development of UHPC. The suggested modeling framework reveals the efficiency of bringing ensemble machine learning along with explainable artificial intelligence methods to provide accurate, reliable, and interpretable predictions of UHPC compressive strength, which creates a useful instrument in the process of mix design optimization and performance evaluation.
A Comprehensive Geotechnical Evaluation of Subsoil Engineering Properties Including Index, Compaction, Shear Strength, and Compressibility Characteristics for Foundation Design and Overall Construction Suitability Assessment Yaser Farman; Saad Hanif; Syed Zamin Raza Naqvi; Muazzam Nawaz; Muhammad Naveed Khalil
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.1

Abstract

The project provides a comprehensive geotechnical evaluation of the geotechnical characteristics of the underground engineering that is relevant to the foundation design and constructability assessment. Edafic samples were sampled at multiple locations and underwent controlled laboratory tests characterized to outline index parameters, compaction behaviour, shear strength coefficients, compressibility traits, consolidation reactions, settlement tendencies, as well as hydraulic permeabilities. The index testing revealed that the soils are mostly under the CH, CL, CI, and NP categories of the Unified Soil Classification System, indicating the large proportion of highly plastic clays, low to intermediate plasticity clays, and non‑plastic granular assemblages. Compaction tests produced the best moisture levels between about 6% and 20% and the highest dry densities of between 1777 kg/m3 and 2341 kg/m3. Parameters of shear strength indicated cohesion values to 111 kPa, and friction angles of 49 o, thus indicating heterogeneous bearing‑capacity regimes. The compression indices of consolidation tests (0.035‑0.070) and settlement projections were moderate, with an overall settlement that falls within the acceptable limits of shallow foundations. Determinations of permeability emphasized a high degree of variability, and in correspondence with the range of grain‑size distribution. Overall, the findings highlight the existence of a heterogeneous subsurface, whose strength and compressibility are moderate, which requires site‑specific foundation plans to maintain the structural integrity and assure the sustainability of the performance in the long term.
Co-Authors Abdul Wahab Imran Ali Channa Muazzam Nawaz Muhammad Khisrow Khan Muhammad Naveed Khalil Syed Zamin Raza Naqvi Yaser Farman