Machine learning for structural engineering: A state-of-the-art review

Despite its multiple benefits, recycled aggregate concrete (RAC) usually exhibits inferior properties compared with natural aggregate concrete, which has been deemed as a hurdle to its widespread use. One way to overcome this roadblock is to apply FRP jacketing. However, it appears not easy to quantify the axial performance of FRP-confined recycled aggregate concrete columns (FRACC), due largely to the complex load-resisting mechanisms involved. Also, the weakness of RAC itself further compound the problem. This paper aspires to deliver an alternative means to address this difficulty. A powerful boosting approach, XGBoost, was developed to fulfill the goal, where its hyperparameters was fine-tuned by a beetle antennae search metaheuristic algorithm. Meanwhile, a synthetic data generator, tabular generative adversarial networks, was introduced to supplement the limited training data. The model developed outperformed existing empirical equations and several baseline machine learning models. Teng et al.’s FRP-confined concrete model was also improved for better tracing the axial stress–strain behavior. Besides, interpreting the model allows better understanding of the underlying mechanisms such as the minimum reinforcement ratio of FRP required to mitigate the negative effects of RAC. Finally, two data-driven, explicit design equations are given for practical design of FRACC. • A novel machine learning model is developed to quantify the axial performance of FRP-confined RAC columns. • Beetle antenna search is hybridized with XGBoost to fine-tune hyperparameters. • The developed model has good performance surpassing existing empirical equations and baseline ML models. • The model is also endowed with explainability that yields useful insights. 

Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks

Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction

Among the many sustainability challenges in the construction industry, those related to the application of concrete and its components are the most critical. Particularly, the production of cement significant threats to pollution, waste, biodiversity, and human health. Sustainable concrete formed by construction & demolition waste (CDW) instead of natural materials has good low-carbon potential, which has been considered as an effective solution to solve the above problems. With the incorporation of new solid waste (supplementary cementitious materials, recycled aggregates and geopolymers, etc.), the design complexity of such concrete becomes increasingly challenging, but the traditional model based on linear regression and assumptions are insufficient to evaluate the performance of the multi-level material system. However, the complex interaction mechanism of these multiple factors is particularly suitable for artificial intelligence (AI) due to its strong nonlinear processing capability. This paper systematically reviewed the research on using AI technology for evaluating sustainable concrete, focusing on mixture ratio, static performance and durability. The results shows that the establishment of a database covering factors such as material composition and curing conditions is crucial to ensure the universality of the prediction model. Machine learning (ML) models considering the influence of multicollinearity can realize the multi objective optimization of concrete mixture and predict the performance of sustainable concrete. On this basis, feature importance analysis can reveal the influence of different input variables on evaluation indicators and overcome the black box problem of the model. Furthermore, this paper discusses the limitations of existing research in detail and proposed feasible schemes in terms of algorithmic mechanisms and performance assessment of sustainable concrete. 

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

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Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming

Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms

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Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms