Review Article
Multiphysics Modelling of Timber-Concrete Composite Structures: A Meta-Analysis of Material Synergies, Coupled Phenomena, and Hybrid Structural Solutions
Girmay Mengesha Azanaw*
Issue:
Volume 2, Issue 1, March 2025
Pages:
1-8
Received:
14 May 2025
Accepted:
7 June 2025
Published:
25 June 2025
Abstract: Timber–concrete composite (TCC) structures have emerged as a sustainable and efficient solution in modern construction, combining the compressive strength of concrete with the tensile performance and ecological advantages of timber. However, their hybrid nature introduces complex modeling challenges due to the interplay between dissimilar materials and multiple physical processes. This meta-analysis provides a comprehensive review of multiphysics modeling approaches applied to TCC systems over the past two decades, synthesizing insights from 48 peer-reviewed studies. The analysis spans structural, thermal, hygric, and time-dependent behaviors to trace the development of simulation frameworks and to identify prevailing trends and persistent limitations. A key finding is the gradual shift towards integrated hygro-thermo-mechanical models, which aim to capture the coupled effects influencing long-term performance. Despite advancements, significant gaps remain, particularly in simulating interface degradation, moisture migration, and time-dependent deformation under service conditions. The review categorizes dominant material pairings and evaluates connection systems, focusing on their performance in both static and dynamic contexts. A comparative stiffness indexing method is introduced to highlight the effectiveness of various modeling strategies and material configurations. Moreover, the review underlines the growing role of digital tools, including finite element techniques and data-driven approaches, in enhancing the predictive accuracy of TCC simulations. It recommends a more unified modeling framework that integrates experimental validation, long-term monitoring data, and AI-enhanced methods to better reflect real-world complexities. The study concludes with a roadmap for future research, emphasizing the importance of robust coupling algorithms, improved interface modelling, and the adoption of hybrid computational-experimental strategies. By consolidating current knowledge and pinpointing unresolved challenges, this review offers a foundational reference for researchers and engineers seeking to advance the modelling of hybrid structural systems. It contributes to the broader goal of optimizing TCC structures for resilience, sustainability, and performance in diverse environmental conditions.
Abstract: Timber–concrete composite (TCC) structures have emerged as a sustainable and efficient solution in modern construction, combining the compressive strength of concrete with the tensile performance and ecological advantages of timber. However, their hybrid nature introduces complex modeling challenges due to the interplay between dissimilar materials...
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Research Article
Artificial Neural Network Modelling of Corrosion Inhibition of Mild Steel in Marine Environment Using Epoxy-Nickel Oxide Nanocomposite Coatings
Issue:
Volume 2, Issue 1, March 2025
Pages:
9-26
Received:
24 January 2025
Accepted:
11 February 2025
Published:
27 August 2025
Abstract: The corrosion inhibition of mild steel in 3.5 wt. % NaCl in the absence and presence of epoxy coatings containing NiO nanoparticles with concentrations of 1.0, 2.0, 3.0 and 5.0 wt. % respectively was studied using the gravimetric technique for a duration of 60 days at room temperature and varying temperatures ranging from 30 to 60°C for 5 hours. The Nickel oxide nanoparticles with average particle size was 23 nm were synthesized by the chemical precipitation technique followed by calcination in a muffle furnace for 3 hours at a temperature of 300°C. Results from the study reveal that epoxy-Nickel oxide nanocomposite coatings are effective green corrosion inhibitors for mild steel in 3.5 wt. % NaCl under different operating conditions and at temperatures within the range of 30 to 60°C. A predictive model based on the Artificial Neural Network (ANN) was developed to study the relationship between the input variables (exposure time, inhibitor concentration and Temperature) and output variables (Corrosion Rate and Inhibition Efficiency). The ANN model was based on the Multilayer Perceptron algorithm with input layer comprising of 3 factors and 23 units. Hyperbolic tangent was used as the activation function for the hidden layer which was made up of 3 units. The output layer with two dependent variables was made up of 2 units. Corrosion test data obtained from 80 experimental runs were successfully modelled using ANN with minimal errors. 56 cases corresponding to 70% of test data were used for training the network and 24 cases corresponding to 30% of test data was used for testing the efficacy of the network. The model had sum of squares error of 0.981, average overall relative error of 0.018 for the training component and values of 3.190 and 0.043 for the sum of squares error and average overall relative error respectively for the testing component.
Abstract: The corrosion inhibition of mild steel in 3.5 wt. % NaCl in the absence and presence of epoxy coatings containing NiO nanoparticles with concentrations of 1.0, 2.0, 3.0 and 5.0 wt. % respectively was studied using the gravimetric technique for a duration of 60 days at room temperature and varying temperatures ranging from 30 to 60°C for 5 hours. Th...
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