Conference Papers
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Item Influence Of Roadcem Content on Mechanical Properties of Lateritic Soil for Pavement Applications(Proceedings of the Third International Civil Engineering Conference (ICEC, 2024), 2025) Illo, N. A.; Abubakar, Mahmud; Abdulrahman, H. S.; Kolo, D. N.Studies on lateritic soil had been carried by numerous researchers across the globe with a view of improving it for the purposes of road pavement and other civil engineering constructions. The main aim of this paper is to examine the impact of varying Roadcem (RC) content on the mechanical properties of lateritic soil for pavement applications which was found to be an A-7-5 soil according to AASHTO. The soil sample was treated with RC at 0, 1,2,3,4, and 5%. Laboratory test such as particles size distribution, unconfined compressive strength (UCS) test for the treated and untreated samples was carried out. Three samples on each dosage were compacted and molded in cylindrical molds and cured two of each sample for 7 days, one each for 28 days. X-ray Diffraction Analysis (XRD), was also carried out on the two samples to reveals their crystalline phases and shows high intensity of CaO and Quartz on the two samples respectively. The UCS results shows insignificant variations in the dosage of RC even at 28 days. The study concluded that A-7-5 soils does not respond to treatment with RC beyond 1% due to its high plasticity and poorly graded and therefore recommend the use of the RC as an additive, at lesser percentage, or on cohesionless soils like sand for road pavement applications.Item Development of Models for Prediction of Soil Cohesion Using Machine Learning Algorithms(Department of Civil Engineering, FUT Minna, 2024-12-12) Muhammed, R. O.,; Adejumo, T. E.; Alhaji, M. M.; Kolo, D. N.; Eze, F. E.Accurate prediction of soil cohesion is crucial for the safe and economical design of geotechnical structures. This study employed five machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting (GB), and Decision Tree (DT)—to predict cohesion (c) using a laboratory dataset of 233 samples. The dataset, augmented to 5000 samples using Getel, was split into 70% training and 30% testing sets. Model performance was evaluated using R-squared and Mean Squared Error (MSE). Results showed that Random Forest outperformed other models, achieving the highest R-squared score of 0.622 and the lowest MSE of 56.74, indicating excellent model fit and high predictive accuracy. Feature importance analysis revealed that plasticity, primarily influenced by Liquid Limit (LL) with an importance score of 0.879606, and Plasticity Index (PI) with an importance score of 1.441646, significantly impacts cohesion. Natural Moisture Content (NMC) also showed significant influence with a score of 0.670434. Particle Size Distribution and Specific Gravity (Gs) also contributed to the predictions. This study demonstrates the potential of machine learning models to enhance the accuracy and efficiency of soil characterization and geotechnical engineering design in predicting soil cohesion.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 Modal Analysis of Barikin Saleh Bridge Deck Using Finite Element Software Simulation Method(Department of Civil Engineering, FUT Minna, 2024-12-12) Rasaq, O. O.; Yusuf, A.; Kolo, D. N.; Abdulrahman, H. S.The increase in traffic along Barikin Saleh area of Minna Niger State calls for the analysis of the bridge deck due to the increasing and fluctuating traffic volume. In this paper, the modal analysis of the Barikin Saleh bridge deck based on finite element software simulation method was studied. The simulation was carried out to determine natural frequencies and the corresponding mode shapes of the bridge deck using ANSYS workbench software. The parameters of the bridge used in the simulation were Length,16m; Width, 10.75m; Second moment of inertia I, 4.16m4; Area A, .56m2; Young’s modulus E, 35300MPa; Density p, 2600 kg/m3, and Concrete Grade G, 50MPa. Based on the simulation output, the bridge exhibited six (6) clear mode shapes and corresponding natural frequencies of 0.299Hz,20.436Hz, 22.875Hz, 25.087Hz, 30.003Hz, and 35.205Hz. The highest natural frequency for the bridge was 35.205Hz, at the bridge deck mid-span. The implication of this is that the lifespan of the bridge might be reduced due to fatigue damage that can occur as a result of repeated loading and unloading of the bridge deck at this frequency. The findings from this study provide valuable insights into the dynamic behavior of Barikin Saleh bridge deck, which can be useful for its maintenance, repair and retrofitting.Item Investigation into the Mechanical properties of concrete using Pebbles from Bida Environ as coarse aggregate in concrete production(Polish Journal of Science, 2024-01-02) Abbas, B. A.; Abdullahi, M.; Sadiku S.; Aguwa J.I.; Abubakar J.; Kolo, D. N.Strength performance remains the most important property of structural concrete, from engineering point of view. This paper investigates the mechanical properties of concrete made from Bida natural stones, extensive experimental work was conducted using 1,600 specimens for compressive strength, flexural strength, splitting tensile strength and elastic modulus (four hundred specimens for each property). Central composite design was used for the factor setting with the following range of values; W/C= 0.4, 0.5, 0.6, CA/TA= 0.55, 0.615, 0.68, TA/C= 3.0, 4.5 and 6.0. The specimens were produced and cured for 7, 14, 21 and 28days. The highest strength was achieved using low W/C, low TA/C and high level of CA/TA corresponding to 0.4, 3.0 and 0.68 respectivelyItem Experimental Study on Steel fibre reinforced Natural aggregate concrete(Ethiopian International Journal of Engineering and Technology (EIJET), 2024-01-02) Kolo, D. N.; Graham, M.; Milad, A.The rising volume of pollution is a significant threat to achieving the United Nations’ goal for a sustainable society. Various approaches have been used to tackle pollution, including recycling wastes into completely new products or utilizing them to improve other materials. In this respect, this article presents the results of an experimental study conducted on waste steel fiber sourced from waste tyres in concrete production. The fibers measuring 2, 4, and 6 cm were utilized using dosages of 0.5, 1, and 1.5% by mass of cement. The natural aggregate which is a bya -a product of the Precambrian deposits of the Bida trough was utilized as coarse aggregate. Iron moulds measuring 150 x 150 x 150mm were used for concrete production and were demoulded after 24 hours and cured. The optimum 28-day compressive strength of 27.19 N/mm2 was recorded with a 4 cm fiber length and 0.5% fiber content. This represented a 36.36% gain in the 28-day compressive strength of the concrete when compared to the control.