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Review Article
Blending Data-Driven Surrogates with Physics - Based Topology Optimization: A Critical Review of Machine Learning - Accelerated Design in Fibre - Reinforced Polymer and Concrete Structures
Girmay Mengesha Azanaw*
Issue:
Volume 10, Issue 3, September 2025
Pages:
80-93
Received:
10 May 2025
Accepted:
29 May 2025
Published:
28 July 2025
Abstract: Blending data driven surrogates with physics based topology optimization holds the promise of revolutionizing the design of fibre reinforced polymer (FRP) composites and concrete structures by dramatically reducing computational cost while preserving-or even enhancing-solution quality. This critical review synthesizes developments from last decade in which machine learning (ML) models such as deep neural networks, Gaussian processes, and ensemble learners have been trained to approximate finite element analyses within iterative optimization loops. The author investigates the applications of Fiber Reinforced Polymer (FRP) composites, wherein the exigencies of continuous fiber orientation and constraints imposed by additive manufacturing necessitate the employment of high-fidelity yet efficient computational solvers. Additionally, The author explore the domain of concrete structures, wherein the inherent heterogeneity, prevalence of cracking, and considerations of durability present distinctive challenges for modeling. By conducting a comprehensive literature review utilizing databases such as Scopus, Web of Science, IEEE Xplore, and MDPI, alongside stringent inclusion criteria, we extract and analyze algorithmic selections, training data configurations, performance metrics (including prediction error and speed-up factors), and outcomes pertaining to manufacturability. The findings indicate that workflows driven by neural surrogate models can achieve accelerations of up to 50 times while maintaining deviations of less than 5% from full-order models; however, limitations in generalizability across various loading scenarios persist. The author delineate critical deficiencies, including the scarcity of benchmark datasets, restricted transfer learning across diverse material systems, and integration challenges with Computer-Aided Design (CAD) and Finite Element Analysis (FEA) frameworks, and The author propose avenues for future research which encompass hybrid physics-based machine learning frameworks and real-time optimization. By elucidating best practices as well as existing challenges, this review offers a strategic roadmap for researchers and practitioners aiming to exploit machine learning-accelerated topology optimization in the advancement of next-generation composite and concrete design.
Abstract: Blending data driven surrogates with physics based topology optimization holds the promise of revolutionizing the design of fibre reinforced polymer (FRP) composites and concrete structures by dramatically reducing computational cost while preserving-or even enhancing-solution quality. This critical review synthesizes developments from last decade ...
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Research Article
Groundwater Quality Investigation in the Coastal Aquifer of Limbe, South West Cameroon
Ewanoge Mesumbe,
Alice Magha Mufur*,
Mathias Fru Fonteh
Issue:
Volume 10, Issue 3, September 2025
Pages:
94-109
Received:
25 March 2025
Accepted:
10 June 2025
Published:
30 July 2025
Abstract: Coastal aquifers are vital fresh water reservoirs that could be affected by seawater intrusion, thereby polluting the water resources. This study investigated the current status of subsurface water in Limbe-Cameroon, focusing on aquifer hydrochemical characteristics. Groundwater samples were obtained from nine boreholes and measurements were conducted on the following physicochemical parameters; pH, electrical conductivity (EC), and total dissolved solids (TDS) and major ions (cations and anions). The results showed that most of the sampled boreholes were in the permissible limits of the World Health Organization (WHO) guidelines, except for a few samples. 11.11% of the pH values, 11.11% of the EC values and 11.11% of the TDS values the WHO recommended limits. Major ion concentrations were below WHO prescribed levels in all analysed samples. The water quality index (WQI) indicated that 44.44% of the samples were of good quality water with water quality values varying from 26-50, 11.11% were classified as poor-quality water and another 11.11% of the samples were unsuitable for drinking purposes. The hydrochemical facies were principally Ca-HCO3 and Ca-Mg-Cl-SO4 water types. Irrigation water quality indices such as sodium adsorption ratio (SAR), Magnesium Hazard (MH), soluble sodium percentage (SSP) indicated that groundwater in Limbe is suitable for irrigation. These higher values signify the possiblity of salt water intrusion in the study area and highlights the critical need for sustainable groundwater management in Limbe to prevent further degradation from seawater intrusion and protect the freshwater resources in the region.
Abstract: Coastal aquifers are vital fresh water reservoirs that could be affected by seawater intrusion, thereby polluting the water resources. This study investigated the current status of subsurface water in Limbe-Cameroon, focusing on aquifer hydrochemical characteristics. Groundwater samples were obtained from nine boreholes and measurements were conduc...
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Research Article
Magnetic Anomalies Induced by Sources with Unknown Geometry
Lady Mireille Razafindranaivo*
Issue:
Volume 10, Issue 3, September 2025
Pages:
110-116
Received:
24 July 2025
Accepted:
4 August 2025
Published:
20 August 2025
Abstract: This research paper addresses the challenges of interpreting magnetic anomalies arising from subsurface sources with unknown or complex geometries, a common issue in geophysical exploration when geological structures deviate from standard, idealized shapes. Traditional inversion methods often rely on geometric assumptions, leading to ambiguities when faced with natural, irregular sources. The study proposes an integrated, geometry-agnostic workflow combining nonparametric equivalent layer modeling, Bayesian Markov Chain Monte Carlo (MCMC) uncertainty quantification, and convolutional neural network (CNN) classification. Synthetic magnetic data generated from amorphous and fractal bodies serve as the basis for validating the method. The equivalent layer inversion reconstructs broad magnetization distributions without the need for explicit geometric constraints, while Bayesian MCMC provides probabilistic estimates and quantifies uncertainty in source parameters such as depth and magnetic moment. This probabilistic approach acknowledges the inherent non-uniqueness of the inverse problem. Additionally, a CNN trained on synthetic datasets can classify magnetic anomalies into source shape categories (bulky, elongated, irregular) with associated uncertainty, enhancing interpretive confidence in complex cases. The study further analyzes sensitivity to noise and magnetization direction variability, demonstrating that these factors critically affect both inversion accuracy and classification performance. Results from synthetic experiments underscore the importance of integrating uncertainty analysis and automated learning in early-stage exploration scenarios, especially when geological information is limited or ambiguous. The proposed framework is shown to enhance the reliability and objectivity of magnetic anomaly interpretation, with future directions involving multi-physics integration and scalable 3D analysis for large regional surveys.
Abstract: This research paper addresses the challenges of interpreting magnetic anomalies arising from subsurface sources with unknown or complex geometries, a common issue in geophysical exploration when geological structures deviate from standard, idealized shapes. Traditional inversion methods often rely on geometric assumptions, leading to ambiguities wh...
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Methodology Article
Environmental Assessment of Heavy Metals Contamination and Radionuclides Exposure in Automotive, Industrial, and Residential Areas in Gboko, Nigeria
Agaku Raymond Msughter*,
Shiada Msugh Stephen,
Bem Timothy Terngu,
Nyijime Simon Ayila,
Aba James Aondolumun
Issue:
Volume 10, Issue 3, September 2025
Pages:
117-129
Received:
11 July 2025
Accepted:
28 July 2025
Published:
21 August 2025
Abstract: This study assessed heavy metal contamination and radionuclide exposure in automobile, industrial, and residential areas in Gboko, Nigeria. The concentration of heavy Metals in the soil of these areas was assessed to determine the presence and activity levels of radionuclides. The evaluation showed the potential health risks associated with exposure to these contaminants and suggested possible remediation and policy recommendations. The study used Atomic Absorption Spectrometry, and soil samples were air dried, while the equipment used to evaluate heavy metals was the Lovibond Tintometer model MD 600. The study used a model equation to analyze the data. Findings revealed that Heavy metal exposure varied across land-use areas, with lead (Pb) levels highest in residential zones (HQ: 0.171) and cadmium (Cd) posing the greatest concern overall, especially in residential areas where the HQ approached 0.380. Chromium (Cr) exposure was most significant in industrial zones, though all hazard quotients remained below hazardous thresholds, while elevated zinc (Zn) levels in automotive workshops stayed within safe limits; radiation levels from Ra-226, Th-232, and K-40 were minimal, with the highest dose in Industrial 1 due to Ra-226 (0.5 Bq/kg) resulting in a TED of 0.2420 mSv/y and ELCR of 0.0847. Nonetheless, all measured values were well below international safety limits, indicating that current heavy metal and radionuclide contamination does not pose significant health risks in the study areas. Proactive environmental monitoring, especially in high-activity zones, is recommended to reduce pollution and safeguard public health.
Abstract: This study assessed heavy metal contamination and radionuclide exposure in automobile, industrial, and residential areas in Gboko, Nigeria. The concentration of heavy Metals in the soil of these areas was assessed to determine the presence and activity levels of radionuclides. The evaluation showed the potential health risks associated with exposur...
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