Civil Engineering
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Item Partial Replacement of Fine Aggregate With Waste Glass in Concrete Made From Bida Natural Aggregate(3rd International Engineering Conference (IEC 2019) Federal University of Technology, Minna, Nigeria, 2019) Alhaji, B.; Kolo, D. N.; Abubakar, Mahmud; Yusuf, A.; Abdullahi, A.; Shehu, M.This study reports the experimental investigation on the suitability of waste glass as partial replacement for fine aggregate in concrete made using Bida natural aggregates (BNA). Glass is widely used in our daily lives through manufactured products such as sheet glass, bottles, glassware, and vacuum tubing. It is an ideal material for recycling. The increasing awareness of glass recycling speeds up inspections on the use of waste glass with different forms in various fields. Mix ratio of 1:2:4 batched by weight with water - cement ratio of 0.55 was used. The percentage replacement varied from 0% to 40% at 5% intervals. Slump test was conducted to assess the workability of the fresh concrete. The compressive strengths and densities of cured concrete cubes of sizes 150mm x 150mm x 150mm were evaluated at 7, 21 and 28days. A total of 81 concrete cubes were cast and tested. It was observed that an increase in the percentage replacement of fine aggregate with waste glass reduces workability, density and compressive strength. The compressive strength and density vary with days of curing. The findings of this study indicated that the optimum replacement percentage of waste glass with conventional fine aggregate was 20%. However, waste glass can effectively be used as fine aggregate replacement (up to40%) without substantial change in concrete strength.Item Artificial Intelligence and Structural Reliability Analysis in Nigeria: A Review(Department of Civil Engineering, FUT Minna, 2024-12-12) Olorunpomi, M. D; Kolo, D. N.; Abdullahi, A.; Agbese, E. O.Reliability is a probabilistic measure of structural safety. In Structural Reliability Analysis (SRA), both loads and resistances are modelled as probabilistic variables, and the failure of structure occurs when the total applied load is larger than the total resistance of the structure. This review presents the recent advances in using Artificial Intelligence (AI) in SRA; it explores the application of Artificial Intelligence (AI) in assessing the structural reliability of structures, particularly focusing on the integration of machine learning models, predictive analytics, and data-driven approaches. AI-based tools can enhance accuracy, speed, and efficiency in structural assessments, offering a potential solution to Nigeria's infrastructure challenges. Machine learning-based techniques have been introduced to SRA problems to deal with its huge computational cost and increase accuracy. ANNs and SVMs are two popularly used tools in the ML-based SRA literature. They have been widely used for the SRA because of their adaptability to different well-known reliability calculation methods such as MCS, FORM, and SORM. While these technologies have been successfully implemented in other parts of the world, its application in Nigeria faces challenges related to data availability, infrastructure, and expertise. Nonetheless, with the increasing adoption of digital technologies in Nigeria’s construction industry, AI offers a compelling opportunity for improving the safety and sustainability of concrete structures.Item PARTIAL REPLACEMENT OF FINE AGGREGATE WITH WASTE GLASS IN CONCRETE MADE FROM BIDA NATURAL AGGREGATE(3rd International Engineering Conference (IEC 2019), 2019-09-22) Alhaji B.; Kolo, D. N.; Abubakar M.; YUSUF, Abdulazeez; Abdullahi, A.; Shehu, M.This study reports the experimental investigation on the suitability of waste glass as partial replacement for fine aggregate in concrete made using Bida natural aggregates (BNA). Glass is widely used in our daily lives through manufactured products such as sheet glass, bottles, glassware, and vacuum tubing. It is an ideal material for recycling. The increasing awareness of glass recycling speeds up inspections on the use of waste glass with different forms in various fields. Mix ratio of 1:2:4 batched by weight with water – cement ratio of 0.55 was used. The percentage replacement varied from 0% to 40% at 5% intervals. Slump test was conducted to assess the workability of the fresh concrete. The compressive strengths and densities of cured concrete cubes of sizes 150mm x 150mm x 150mm were evaluated at 7, 21 and 28days. A total of 81 concrete cubes were cast and tested. It was observed that an increase in the percentage replacement of fine aggregate with waste glass reduces workability, density and compressive strength. The compressive strength and density vary with days of curing. The findings of this study indicated that the optimum replacement percentage of waste glass with conventional fine aggregate was 20%. However waste glass can effectively be used as fine aggregate replacement (up to 40%) without substantial change in concrete strength.Item Models to Predict the Fresh and Hardened Properties of Palm Kernel Shell Concrete(Malaysian Journal of Civil Engineering, 2022-01-12) Kolo, D. N.; Tsado, T. Y.; Abbas, B. A.; Adamu, H. N.Concrete is an assemblage of Cement, aggregates and water, the most frequently used fine aggregate for concrete production is sand sourced from river banks. The continuous exploitation of available granite conventionally used as coarse aggregate in concrete production coupled with rapid infrastructural development has resulted in its scarcity and often high cost. The suitability of utilising Palm Kernel Shell (PKS) as partial replacement for coarse aggregate in concrete production was examined in this paper. Preliminary tests were conducted on all aggregates to determine their suitability for concrete production. Concrete with 5, 10, 15, 20 and 25% PKS-coarse aggregate content was cast with a mix ratio of 1:2:4. The freshly prepared Palm Kernel Shell Concrete (PKSC) was cast in moulds measuring 150 x 150 x 150mm and cured using ponding method. The Compressive strength result shows that an increase in the PKS content results in a decrease in compressive strength of concrete. Linear regression models for the slump and compressive strength of the PKSC were developed and found to be sufficient in predicting the compressive strengths with R2 values of 96% and 92 % respectively.