https://ajesa.com.ng/index.php/AJESA <p>ADVANCED JOURNAL OF ENGINEERING AND SCIENTIFIC APPLICATIONS (AJESA) is an academic/technical Journal of engineering and applied sciences published by the Nigerian Institute of Electrical and Electronic Engineers (NIEEE) Awka Chapter covering all fields of Engineering and Engineering Sciences such as Mechanical Engineering, Safety and Fire Engineering, Civil Engineering, Electrical and Electronic Engineering, Computer Engineering, Communication Engineering, Computer Science, Software Engineering, Cybersecurity, Information Technology, Entrepreneurship Studies, Chemical Engineering, Petrochemical Engineering, Water Engineering, Highway Engineering, Materials and Metallurgical engineering, current trends in Artificial Intelligence, Robotics and Machine Learning, Polymer Engineering, etc.</p> <p>The Journal is an open-access online journal publishing double-blind peer-reviewed research papers in every area of engineering and scientific applications related to engineering and engineering sciences and sub-fields. It is published every month within two-months interval, but paper submission is open every month.</p> en-US drfcobodoeze@gmail.com (Engr. Dr. Fidelis C. Obodoeze PhD, MNSE, NIEEE, FIIA) vc.onuzulike@unizik.edu.ng (Engr. Dr. Vincent C. Onuzulike PhD,MNSE,NIEEE ; Department of Electronic and Computer Engineering, Nnamdi Azikiwe University, Awka, Nigeria) Fri, 14 Aug 2026 00:00:00 +0000 OJS 3.3.0.7 http://blogs.law.harvard.edu/tech/rss 60 https://ajesa.com.ng/index.php/AJESA/article/view/59 The California Bearing Ratio (CBR) test is a penetration test used to evaluate the subgrade strength of roads and pavements. Results from the CBR test are used with empirical curves to determine the thickness of pavement layers, and the test remains the most widely used method for the design of flexible pavements. Under normal conditions, obtaining the result of a soaked CBR test takes between four and five days, a duration that can be impractical in emergency situations. During the COVID-19 pandemic, for example, pressure to deliver infrastructure quickly led to the construction of a 1,000-bed Huoshenshan Hospital in Wuhan, China, in ten days (24 January to 2 February 2020); achieving comparable feats depends on faster means of estimating soil strength. This study therefore explores an indirect, regression-based approach to predicting CBR. Geotechnical test results for 84 soil samples were collected, and a multiple regression analysis was carried out with the soaked CBR value as the dependent variable and existing moisture content (EMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), particle size distribution, optimum moisture content (OMC), and maximum dry density (MDD) as independent variables. The analysis produced the relationship Y = −0.57359169X₁ + 0.677530201X₂ – 0.64691219X₃ – 0.59578021X₄ + 0.004879034X₅ – 0.01717437X₆ + 0.13710102X₇ – 0.266139X₈ – 0.10248341X₉ + 0.118277058X₁₀ – 0.10766766X₁₁ + 0.25140518X₁₂ – 0.16878082X₁₃ + 7.543637025X₁₄ – 0.20878597X₁₅ + 5.381442462X₁₆ + 10.18311796, where Y is the predicted soaked CBR value and X₁ through X₁₆ are the corresponding independent variables. The equation was used to predict CBR values for the remaining samples, achieving an accuracy of approximately 92%. A reliability analysis was subsequently carried out on each independent variable; the percentage passing sieve No. 200 and the maximum dry density returned R² values of 0.9872 and 0.9691, respectively, against the coefficient of variation. T. Aleyemi, J. E. Sani, S. A. A. Jibril, V. M. Ishaku Copyright (c) 2026 ADVANCED JOURNAL OF ENGINEERING AND SCIENTIFIC APPLICATIONS https://creativecommons.org/licenses/by-nc/4.0 https://ajesa.com.ng/index.php/AJESA/article/view/59 Fri, 14 Aug 2026 00:00:00 +0000 https://ajesa.com.ng/index.php/AJESA/article/view/60 Wheel alignment is a critical aspect of vehicle maintenance that significantly influences road safety, steering stability, fuel efficiency, tire longevity, and overall vehicle performance. Conventional wheel alignment technologies, including mechanical, laser-based, Charge-Coupled Device (CCD), and three-dimensional (3D) camera systems, provide reliable measurements but are often expensive, require specialized hardware, frequent calibration, and skilled technicians. These limitations reduce their accessibility, particularly for small and medium-sized automotive workshops and educational institutions. This study proposes the design and implementation of a deep learning-based vision system for automated vehicle wheel alignment using computer vision and artificial intelligence techniques. The proposed framework integrates digital image acquisition, camera calibration, image preprocessing, object detection, feature extraction, and geometric measurement algorithms to estimate principal wheel alignment parameters, including camber, caster, and toe angles, without the need for wheel-mounted sensors or reflective targets. An experimental research design was adopted, comprising system development and performance evaluation phases. Vehicle wheel images were collected from multiple passenger vehicles under varying lighting conditions, camera viewpoints, and workshop environments. The dataset underwent preprocessing through image resizing, normalization, augmentation, and annotation before training a deep learning object detection model. Camera calibration techniques were employed to correct lens distortion and establish accurate geometric relationships between image coordinates and real-world measurements. The trained model detected wheel features, extracted rim centers and reference points, and estimated alignment angles, which were subsequently compared with measurements obtained from a commercial wheel alignment machine. The system also achieved real-time processing capability, requiring approximately 100 ms per image and operating at about 30 frames per second on a GPU-enabled platform, while maintaining moderate computational resource utilization. Performance evaluation under varying lighting conditions, camera distances, wheel sizes, and vehicle categories confirmed the robustness and generalization capability of the proposed approach. The study demonstrates that deep learning and computer vision can provide an accurate, low-cost, and automated alternative to conventional wheel alignment systems. By reducing dependence on proprietary hardware and minimizing human intervention, the proposed system offers improved accessibility, diagnostic consistency, and operational efficiency. Samuel Goodliffe Dickson, Madubuezi Christian Okoronkwo Copyright (c) 2026 ADVANCED JOURNAL OF ENGINEERING AND SCIENTIFIC APPLICATIONS https://creativecommons.org/licenses/by-nc/4.0 https://ajesa.com.ng/index.php/AJESA/article/view/60 Sat, 15 Aug 2026 00:00:00 +0000