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All Journal Journal of ICT, Design, Engineering and Technological Science
Syed Zamin Raza Naqvi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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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.
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 Asad Ullah Khan M. Adil Khan Muazzam Nawaz Muhammad Naveed Khalil Saad Hanif Yaser Farman