Browsing by Author "Ifeyinwa Ijeoma Obianyo"

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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.
Characterization and assessment of selected agricultural residues of Nigerian origin for building applications
(Taylor and Francis, 2025-12-31) Esther Nneka Anosike-Francis; Gina Odochi Ihekweme; Paschal Ateb Ubi; Ifeyinwa Ijeoma Obianyo; Seun Jesuloluwa; Adekunle Akanni Adeleke; Prabhu Paramasivam; Azikiwe Peter Onwualu; Rasoamalala Vololonirina
The high rate of agricultural residue generation in Nigeria in recent times poses a serious environmental hazard. Thus, there is a need to valorize these residues for various engineering applications. Five Nigerian agricultural residues (okro, plantain, jute, kenaf, and sisal) were studied to determine their potential for forming natural fiber composites for building applications. The samples were subjected to a process of peeling and immersion in water for 15–20 days to facilitate the degradation of microbial cells and ease the extraction of fibers. Proximate and lignocellulose analyses of the samples were conducted according to the American Standard for Testing and Materials (ASTM) specifications. The physico-mechanical and thermal properties of the agricultural residues were examined using an Intron universal testing machine and a thermogravimetric analyzer. The fiber phase analysis revealed a crystallinity index range of 41.20–66.08% and a crystallite size of 30.79–84.00 nm, indicating that the fibers were thermally stable above 280 C. Fourier Transform Infrared analysis provided conclusive evidence of the presence of distinct chemical compositions and their associated functional groups. The study contributes a reliable database for agricultural residues in Nigeria, particularly for construction applications. It is also being utilized to inform the design and implementation of manufacturing processes for roofing tiles and boards intended for general applications
Characterization and assessment of selected agricultural residues of Nigerian origin for building applications
(COGENT ENGINEERING, 2024-12-22) Esther Nneka Anosike-Francis; Gina Odochi Ihekweme; Paschal Ateb Ubi; Ifeyinwa Ijeoma Obianyo; Seun Jesuloluwa; Adekunle Akanni Adeleke; Prabhu Paramasivam; Azikiwe Peter Onwualu; Rasoamalala Vololonirina
The high rate of agricultural residue generation in Nigeria in recent times poses a serious environmental hazard. Thus, there is a need to valorize these residues for various engineering applications. Five Nigerian agricultural residues (okro, plantain, jute, kenaf, and sisal) were studied to determine their potential for forming natural fiber composites for building applications. The samples were subjected to a process of peeling and immersion in water for 15–20 days to facilitate the degradation of microbial cells and ease the extraction of fibers. Proximate and lignocellulose analyses of the samples were conducted according to the American Standard for Testing and Materials (ASTM) specifications. The physico-mechanical and thermal properties of the agricultural residues were examined using an Intron universal testing machine and a thermogravimetric analyzer. The fiber phase analysis revealed a crystallinity index range of 41.20–66.08% and a crystallite size of 30.79–84.00 nm, indicating that the fibers were thermally stable above 280 °C. Fourier Transform Infrared analysis provided conclusive evidence of the presence of distinct chemical compositions and their associated functional groups. The study contributes a reliable database for agricultural residues in Nigeria, particularly for construction applications. It is also being utilized to inform the design and implementation of manufacturing processes for roofing tiles and boards intended for general applications
Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber
(MDPI, 2024-08-26) Moussa Mahamat Boukar; Ifeyinwa Ijeoma Obianyo
This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties.
Evaluation of the Mechanical and Durability Properties of Marble Waste-Modified Rigid Pavement Material
(MDPI.com, 2025-04-04) Ifeyinwa Ijeoma Obianyo; Maurice Simon Nwaforcha; Kudu Yusuf; Sanusi Abdulganiyu; Abubakar Dayyabu; Musa Umar Kolo; AzikiwePeterOnwualu2
One of the environmental concerns today is the increasing amount of waste generated from marble quarrying and processing. This study evaluates the mechanical and durability properties of marble waste-modified rigid pavement material. A series of laboratory tests was conducted to obtain the properties of marble waste-modified rigid pavement material. The slump value decreases as the percentage of marble waste increases. As the percentage of marble waste increases, the dry density gradually decreases from 2770 kg/m3 to 2590 kg/m3. Comparison of the 7-day and 28-day compressive strength indicates that replacing the gravel with marble waste resulted in early strength gain, making it suitable for use in conditions that require early strength gain. The scanning electron microscopy results indicated higher calcium content for the 10% marble waste sample, which is responsible for the cementation and supports the higher compressive strength obtained for the sample at 7 days of curing, due to early strength gain. The study is the first to show the synergistic effect of marble waste on early strength and durability in rigid pavements These findings. showed that marble waste can be used as a modifier in rigid pavement materials. The study contributes to Sustainable Development Goals 9 and 11