1. Introduction
Concrete confinement using fiber-reinforced polymer (FRP) jackets is widely applied in structural retrofitting. FRP confinement can strengthen reinforced concrete columns by increasing the axial load carrying capacity, the shear and flexural strengths and the performance under seismic loads [
1]. Their resistance to corrosion, light weight and ease of construction are further benefits of using FRP materials instead of concrete or steel jackets [
2]. As reinforcement materials, carbon fibers are mostly used. However, carbon fibers are known to have less ductility compared to materials such as steel or glass. Aramid fiber reinforcement has attracted attention in recent years, due to its favorable combination of ductility and strength. It is reported that aramid fibers have better strength properties than glass fibers and better ductility than carbon fibers [
3]. The performance of FRP-wrapped concrete columns and composite structures in general, such as laminated composite plates, has been extensively studied under various loading conditions. Shih et al. [
4] investigated the application of aramid fiber-reinforced polymers (AFRP) in soft body armor systems. The impact resistance, energy absorption and deformation properties of these systems have been tested using ballistics tests. It was reported that polyurea elastomer processing of Kevlar plates reduces the weight and thickness of multilayered soft body armors and improves the impact resistance. Alkhatib et al. [
5] compared the performances of Kevlar, date palm, carbon, and hybrid fibers used in armor plates in terms of energy absorption with finite element analysis. The hybrid composite plates were found to perform best in terms of energy absorption. The usage of shear thickening fluid impregnation to enhance the penetration resistance of AFRP armor plates was proposed by Lee et al. [
6]. The addition of shear thickening fluid was shown to improve the flexibility, reduce the thickness and enhance the impact resistance of the armor plates. Wang et al. [
7] tested the impact resistance, flexibility and single yarn pull-out resistance of aramid fabrics impregnated with shear thickening fluid and adhesive polyurethane. The aramid fabrics impregnated with both shear thickening fluid and polyurethane were shown to perform better in terms of impact resistance. Another study with hybrid composites was carried out by Meliande et al. [
8], who demonstrated the application of natural curaua fibers, in combination with aramid fibers in laminated composite plates. Increasing the number of curaua-reinforced layers was shown to reduce the flexural strength, energy absorption, and stiffness. Fiber-reinforced polymers have also found applications in the area of pile splicing. Khedmatgozar Dolati and Mehrabi [
9,
10] implemented FRP materials in the splicing of prestressed precast concrete piles. A new FRP-based splicing method has been proposed as an economical and corrosion-resistant splicing technique which satisfies the established design requirements.
The application of FRP wraps to increase the load-carrying capacity of reinforced concrete structures has certain advantages, such as corrosion resistance, a light weight, high stiffness and easy adaptability to the shape of the structure [
11]. Choi et al. [
12] investigated the effect of using FRP wire wraps on the structural behavior of concrete. The application of FRP wire wraps was found to increase the peak strength and reduce volumetric expansion. Iacobucci et al. [
13] carried out experiments with full scale reinforced concrete columns with insufficient transverse steel reinforcement, retrofitted with carbon fiber- and glass fiber-reinforced polymer wraps. The specimens were tested under lateral cyclic loading. During cyclic loading, it was observed that adding more FRP layers enhances the stiffness and strength. It was also shown that deficient columns properly retrofitted with CFRP can perform better than sufficiently reinforced columns without CFRP wraps. Yang et al. [
14] tested AFRP-confined concrete specimens under high strain rate compression. The effect of changing the confinement layer thicknesses was also investigated. The AFRP confinement was shown to improve the dynamic strength, ultimate strain and impact resistance of the tested specimens. Xie et al. [
15] conducted axial compression experiments on square reinforced concrete columns confined with basalt FRP wraps. The basalt–FRP confinement was shown to improve the compressive strength and ductility.
It is known that FRP wraps have greater effectiveness in circular columns compared to rectangular columns. In rectangular columns, the effectiveness of confinement is limited to the areas surrounding the corners [
2]. Sharma et al. [
16] investigated the effect of changing the corner radius on the structural behavior of rectangular columns confined with glass fiber-reinforced polymer (GFRP) wraps. It was concluded that columns with a corner radius equal to concrete cover have better results, in terms of axial load carrying capacity, compared to columns with a corner radius less than the concrete cover. Due to the differences in the confinement of rectangular and circular columns, different sets of equations have been proposed in the literature for the prediction of the compressive strength and ultimate strain of FRP-wrapped columns with circular and rectangular cross-sections. Some of these equations do not distinguish between different fiber materials [
17,
18,
19,
20], whereas others are developed specifically for FRP wraps made of aramid fibers [
21,
22,
23,
24].
In recent years, machine learning techniques have been increasingly applied in the field of predicting the mechanical properties of concrete structures. In particular, the effects of different ingredients such as fly ash, silica, or ceramic waste powder on the compressive strength of concrete have been investigated, using techniques such as ensemble machine learning, artificial neural networks and gene expression programming [
25,
26,
27]. Ahmad et al. [
28] investigated fly ash-based geopolymer concrete with respect to its compressive strength using the AdaBoost, bagging regressor and decision tree algorithms. The bagging regressor was found to be the most accurate among these algorithms in predicting the compressive strength. Shang et al. [
29] used decision tree and AdaBoost algorithms to predict the compressive strength and splitting tensile strength of recycled coarse aggregate-based concrete. A coefficient of determination of 0.95 could be achieved from the AdaBoost algorithm. The applicability of the gene expression programming algorithm in compressive strength prediction was shown by Ahmad et al. [
30] and Farooq et al. [
31] for recycled aggregate concrete and high strength concrete, respectively. The splitting tensile strength of recycled aggregate-based concrete was also investigated by Amin et al. [
32]. Decision tree, random forest and artificial neural network techniques were applied. It was shown that the random forest algorithm outperformed the decision tree and artificial neural network algorithms.
In this study, the performances of the equations from the literature developed for aramid fiber-reinforced polymer (AFRP)-wrapped circular columns were compared to the predictions of the machine learning (ML) algorithms. A dataset of 225 samples with circular cross-sections was collected from the literature [
3,
21,
22,
24,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49] which can be found in
Supplementary Materials (under the GitHub link
https://github.com/ccakiroglu/AFRP (accessed on 27 October 2023)). A 159-sample subset of this dataset was used for the training of ML models for the prediction of ultimate strain. The experimental datasets were further enhanced using the tabular generative adversarial network (TGAN) methodology. It was shown that the currently available predictive equations in the literature can be significantly inaccurate when applied to new datasets. As an alternative approach to predictive equations, ML models were presented as a means of making accurate predictions about structural behavior. The impacts of different input features on the model predictions were quantified using the SHapley Additive exPlanations (SHAP) method, and the input features were ranked according to their impacts on the model predictions. Furthermore, the changes in the model predictions, with respect to the most impactful input features, were visualized using individual conditional expectation (ICE) plots. Due to the favorable mechanical properties of aramid fiber-reinforced polymers (AFRP), these materials are finding applications in various areas of structural engineering. Therefore, the availability of reliable and accurate prediction methods for the mechanical properties of AFRP-confined concrete structures is crucial.
The current study presents a novel approach to predicting structural behavior, with the help of explainable ensemble ML models. The effect of different parameters on the mechanical behavior of AFRP-confined concrete is visualized using the SHAP algorithm. Furthermore, an alternative approach is proposed for generating large synthetic datasets, which could be utilized to train predictive models. The study is distinctive in that it aims to prepare big datasets, with the help of tabular generative adversarial networks, for applications in the prediction of the mechanical properties of composite materials. In the case of FRP wraps, the identification of mechanical properties necessitates costly experimental procedures or finite element modelling. Therefore, generative machine learning techniques present a valuable alternative to these traditional techniques for generating large datasets. The availability of large datasets is vital for the application of ML techniques. Therefore, the study makes a significant contribution towards the adoption of ML techniques in modelling engineering systems.
4. Discussion
FRP materials are finding increasing numbers of applications in many areas of engineering in recent years. Particularly in structural retrofitting, the application of FRP jackets is a widely used alternative to steel confinement. As an alternative to carbon and glass fibers, aramid fibers are also finding applications in this area, due to their favorable ductility and strength properties. However, the lack of reliable equations can be a significant hurdle in the way of wide-scale applications of these materials in engineering design.
The current study showed the application of state-of-the-art ensemble learning techniques in mechanical properties predictions of AFRP-confined concrete. A dataset gathered from the literature served as the basis for training the machine learning models. The reliability and accuracy of machine learning models can be increased in proportion to the size of their training dataset. Based on this assumption, the current study proposes a novel technique of generating additional data points using generative adversarial networks (GANs). These models consist of a generator and a discriminator component, where the performance of the generator component iteratively improves, such that the newly generated data points are indistinguishable from the original data points. It was shown that the predictive models trained on the dataset enhanced with the synthetic data points can significantly outperform the predictive equations currently available in the literature. One of the major reasons for the inaccuracy of the equations is that most of the equations in the literature are developed based on regression techniques fine-tuned towards a particular dataset and therefore cannot be generalized sufficiently. Therefore, it is crucial to train any statistical model on a large enough dataset.
In the next stage of the study, the machine learning models have been further investigated using the SHapley Additive exPlanations (SHAP) approach. The SHAP results provide explanations for the predictive models by measuring the contribution of different input features to the model output. Using the SHAP technique, the input features have been ranked with respect to their impact on the model predictions for the confined compressive strength and ultimate strain. The unconfined compressive strength of concrete and the ultimate tensile strength of the AFRP wrap have been found to be the most impactful input features in the prediction of the confined compressive strength and ultimate tensile strain, respectively. Finally, the variations of the confined compressive strength and ultimate strain, with respect to different input features, have been visualized using individual conditional expectations plots.