Department of Computer Science

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    A Machine Learning Led Investigation Predicting the Thermos‑mechanical Properties of Novel Waste‑based Composite in Construction
    (Waste and Biomass Valorization, 2024-05-04) Assia Aboubakar Mahamat; Moussa Mahamat Boukar; Ifeyinwa Ijeoma Obianyo; Nurudeen M. Ibrahim
    The study explores the potential of machine learning (ML) in predicting the thermal and mechanical properties of earth-based composites reinforced with natural Borassus fruit fiber. The limited availability of large datasets for accurate predictions is a challenge in material science research, which this study addresses. The authors collected data on thermal conductivity, compressive and flexural strength through experiments and employed four ML techniques suitable for small datasets: linear regression (LR), random forest (RF), decision tree regressor (DTR), and gradient boosting (GB). Evaluation metrics were used to assess the performance of the ML techniques. Linear regression emerged as the most efficient, exhibiting significantly lower error values compared to the others (e.g., RMSE of 0.066 for thermal conductivity, 0.119 for compressive strength, and 0.04 for flexural strength), followed by random forest and decision tree. However, gradient boosting showed relatively poor predictive accuracy. This study demonstrates the successful application of ML for predicting the properties of earth-based composites with limited data, which could significantly reduce the cost and time associated with developing new building materials and products. Manufacturers can gain a competitive edge by using ML to streamline material development, leading to lower costs, faster innovation, and the creation of more environmentally friendly building materials for a greener construction sector.
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    Machine learning techniques versus classical statistics in strength predictions of eco-friendly masonry units
    (IEEE, 2021-02-02) Assia Aboubakar Mahamat; Moussa Mahamat Boukar
    Earth-based materials demonstrated promising characteristics in the development of eco-friendly, low cost and sustainable construction materials. However, their unconventional utilization in construction makes the assessment of their properties very difficult and inaccurate because they are assessed based on conventional materials procedures. Hence, the properties of earth-based materials are not well understood. The assessment of earth-based materials properties for sustainable construction is time-consuming, expensive, and inaccurate. To obtain more accurate properties, an artificial neural network and statistical linear regression analysis were used to predict the compressive strength of alkali-activated soil. Statistical linear regression analysis was carried out to compare the efficiency of the machine learning technique with the classical statistics model. Parameters such as Si/Al, activator level, curing temperature, water absorption, and weight were used as input parameters to predict the target variable. The coefficient of determination was used to examine the performance of the models. The results depict that artificial neural network outperformed statistical linear regression analysis with R2=0.74, RMSE=0.119 and R2 =0.48, RMSE=0.466 respectively. This indicates that statistical linear regression analysis is inefficient for prediction of the strength in alkali activated soils