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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.
Deep Learning Architectures for Concrete Compressive Strength Prediction: A State‑of‑the‑Art Review of CNN, ANN, and Hybrid Models M. Adil Khan; Imran Ali Channa; Saad Hanif; Baitullah Khan Kibzai
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-9.1.7

Abstract

Structural safety, optimization of materials, and sustainable construction practice depend on the prediction of concrete compressive strength. Traditional methods of testing use the laboratory method, which is time‑consuming, expensive, and destructive. Recent progress in deep learning has made it possible to predict the compressive strength in an accurate, rapid, and non‑destructive way by modeling nonlinear complex relationships between the constituents of concrete, curing conditions, and mechanical performance. This review is a systematic review of the deep learning architectures that have been applied to predict the concrete compressive strength with state‑of‑the‑art, such as Convolutional Neural Networks (CNNs), Artiϐicial Neural Networks (ANNs), Deep Neural Networks (DNNs), Long Short‑Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Transformer‑based models, and hybrid architectures (CNN‑LSTM, CNN‑GRU, and ensemble stacking). It has been shown in the literature that higher hybrid and ensemble models allow the high predictive performance to be achieved, with the value of R² often exceeding 0.95, with the best possible models having an R² of 0.99 when using controlled datasets. Both metaheuristic optimization algorithms (e.g., PSO, GA, ACO, TLBO) and Bayesian hyperparameter tuning would greatly increase the model generalization and robustness. Moreover, interpretable artificial intelligence tools, such as SHAP and sensitivity analysis, have enhanced interpretability, and cement content, curing age, and water‑cement ratio are confirmed to be the most significant predictors of strength. Applications have been spread over the spe‑ cialized materials like ultra‑high‑performance concrete (UHPC), geopolymer concrete, recycled aggregate concrete, self‑compacting concrete, and waste‑based sustainable concretes. However, the issues of data standardization, cross‑laboratory generalization, and model transparency persist in spite of impressive advances. The future research is to be directed at physics‑informed neural networks, the multi‑objective optimization that considers the metrics of environmental impact, real‑time edge deployment, and the standardized benchmark datasets. In general, methods using deep learning as its core technology can be discussed as a revolutionary development in intelligent concrete design and sustainable construction engineering
Sustainable Concrete Mixture Design for Reducing Embodied CO₂: A Comprehensive Data‑Driven Assessment of Material Composition, Environmental Indicators, and Predictive Modeling for Low‑Carbon Construction Applications M. Adil Khan; Asad Ullah Khan; Saad Hanif; Syed Zamin Raza Naqvi
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-9.2.4

Abstract

Minimising embodied CO2 in concrete is one of the most important tasks that needs to be performed in order to attain a sustain‑able construction and prevent the effects of the changes in climatic conditions. This paper is a comparative analysis of three machine learning models: Linear Regression (LR), AdaBoost (ADB), and K‑Nearest Neighbours (KNN) to predict embodied_CO2 based on a dataset of 1,000 ob‑servations in the form of mixture composition, material properties, and environmental indicators. The descriptive statistical analysis assured the balanced distribution of most variables with little skewness, whereas the correlation analysis revealed cement and resource consumption as the leading factors contributing to embodied_CO2. Training, testing, split, and k‑fold cross‑validation based on the R, MAE, RMSE, RAE, and RRSE metrics were used to measure the model performance. Findings reveal that KNN was a better method in comparison with LR and ADB in all assessment systems. KNN with k‑fold validation had a correlation coefficient of 0.9996, MAE of 1.8668, and RMSE of 2.5041 versus LR (R = 0.9874, MAE = 11.3218, RMSE = 13.0931) and ADB (R = 0.9764, MAE = 14.5647, RMSE = 18.0974). The same tendencies were noted in the testing stage, with KNN having R = 0.9996, MAE = 1.9273, and RMSE = 2.7044, which are considerably lower than LR (MAE = 11.0947; RMSE = 12.8293) and ADB (MAE = 13.9921; RMSE = 16.8487). The residual analysis also indicated that KNN has better stability, with tightly clustered and symmetric error distributions and a small generalisation gap. The results show that instance‑based learning is effective to learn complex nonlinear associations in embodied carbon prediction. This paper emphasizes the significance of strong cross‑validation and residual diagnos‑tics in model selection and shows the feasibility of machine learning in aiding the design of low‑carbon concrete with regard to design strategies.
Co-Authors Abdul Wahab Asad Ullah Khan Baitullah Khan Kibzai Imran Ali Channa M. Adil Khan Muazzam Nawaz Muhammad Khisrow Khan Muhammad Naveed Khalil Syed Zamin Raza Naqvi Yaser Farman