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Over the world, there is growing worry about the corrosion of reinforced concrete structures. Structure repair, rehabilitation, replacement, and new structures all require cost-effective and long-lasting technologies. Fiber Reinforced Polymer (FRP) has been widely employed in both retrofitting existing structures and building new ones. Due to its v...
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... there is no appropriate code for the experimental investigation of FRP-concrete bond strength, and prior studies have only established a few traditional test configurations, such as beams bending tests, single and double shear tests. The bond strength testing setup is depicted in Figure 1. The collected database contains both single and double shear 744 samples results and parameters which includes f ′ c is the concrete with specified compressive strength (MPa), b f is the width of the FRP laminate/fabric (mm), E f is the modulus of elasticity of FRP material (GPa), t f is the thickness of FRP material (mm), b c is the width of concrete block (mm), f f is the tensile strength of FRP composite (MPa) and L b (mm) is the length FRP bonded material are tabulated in Table 1. ...
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... Concrete jacketing results in enlarged areas by casting additional concrete over the entire portion of the column, whereas steel jacketing is more susceptible to internal corrosion, incorporates antirust work, and has handling issues 9 . Fiber-reinforced polymer (FRP) is a composite material that addresses the shortcomings of traditional retrofitting methods and exhibits several advantages namely; superior capacity, low weight, corrosion resistance, high endurance, and easy usage in the field 10 . Owing to their structural advantages such as resistance to corrosion, FRP has been employed in a diverse range of structural elements such as beam, column, beam-column joint, slab, etc. ...
The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.
... The potential of using ML models for engineering problems was highlighted. Kumar et al. [48] utilized ANN, artificial bee colony-ANN (ABC-ANN), and GPR techniques to investigate the FRP-concrete bond strength. The ABC-ANN hybrid method is used to optimize the ANN model for improved bond strength predictions. ...
In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.
... A machine learning regression model was developed to predict the bond strength and effectiveness of this method [1]. Concrete and reinforced concrete structures, strengthened with carbon fiber-reinforced plastic (CFRP), have complex strain and failure patterns that need simplified predictive models rather than complicated finite-element analyses and experiments [1,2]. In [2], such models are (1) a neural network, (2) a heuristic search optimization algorithm simulating the behavior pattern of a bee colony, and (3) Gaussian process regression. ...
... Concrete and reinforced concrete structures, strengthened with carbon fiber-reinforced plastic (CFRP), have complex strain and failure patterns that need simplified predictive models rather than complicated finite-element analyses and experiments [1,2]. In [2], such models are (1) a neural network, (2) a heuristic search optimization algorithm simulating the behavior pattern of a bee colony, and (3) Gaussian process regression. Fiber-reinforced mortar can be used to repair and restore building structures. ...
... where , f are the relative deformation and deflection of a structure as a result of an emergency situation, respectively; u is the relative deformation corresponding to the temporary resistance (ultimate strength) of steel; ult f is the deflection at which material assets and humans can be safely evacuated from a building. Conditions (2) and (3) can be applied to predict the emergency-triggered behavior of a deformed structure subjected to displacements in the process of operation. ...
Actual load identification is a most important task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged structural frames, and (3) structural health monitoring. Actual load values are used to determine the stress–strain state (SSS) of a structure and accomplish various engineering objectives. Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the article presents an integrated method combining basic provisions of structural mechanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make projections, and assembling structures to make final projections for steel frame structures subjected to elastic strain. Final projections serve to identify parameters of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient matrices applied to unit loads and (2) actual loads standardized using maximum load values. Cases of neural network training and parameters identification are provided for simple beams. The aim of this research is to enhance the reliability and durability of steel structures by predicting consequences of unfavorable load, including emergency impacts. The novelty of this study lies in the co-use of artificial intelligence elements and structural mechanics methods to predict load parameters using actual displacement curves of structures. This novel approach will enable engineering inspection teams to predict unfavorable load peaks, prevent emergency situations, and identify actual causes of emergencies triggered by excessive loading.
... The results showed that 22% of the reviewed studies discussed the application of different ML techniques for FRP-concrete bond strength. Within this scope, many ML models have been created to compare the feasibility of various algorithms [88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103]. ...
... In addition, a combination of ANN and Artificial Bee Colony (ABC) was proposed as a new ML method to estimate the interfacial bond strength between concrete and FRP [89,95,104]. In these studies, the results of large datasets of single and double-lap shear tests were collected from the available published articles to train the developed ABC-ANN models. ...
Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.
... The load transmission capacity of an FRP-to-concrete connection depends on the integrity of the bond between the FRP and the concrete substrate [154], which can be affected by both loading and environmental factors. Several researchers [155][156][157][158][159][160][161][162][163][164] have investigated the feasibility of using ML to simulate the bond strength of FRP-toconcrete joints. However, the developed models can only be employed using unconditioned data, therefore, they cannot be used to investigate the durability of conditioned connections [165]. ...
... Te inputs used to solve the problem are multiplied by appropriate weights, and these weighted inputs are summed with the bias value [53]. Te input sums are processed in hidden layers using transfer functions such as linear, tan-sigmoid, and log-sigmoid [112]. Information processed by the transfer function is sent along the output layer as the desired result. ...
Cement manufacturing and utilization is one of the majorly responsible for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCM) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. Compressive strength (CS) of concrete is important that is used to evaluate the material's strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864 and 0.9545, and 1.6471 MPa, 2.7149 MPa and 4.4334 MPa, respectively. The results obtained from this study have been found promising and enrich the available literature. This work will nudge civil engineering and material science researchers in opting for sustainable computing techniques. However, the study's limitations include the need for additional research into the material's long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.
... The authors used threshold logic-based methods to develop ML approach for neural networks. The main purpose of an ANN is to transfer data or information from one source to another and make a relationship between them, this is like the biological nervous system (human brain) [35]. Currently, the applications of ANN are used in various fields. ...
Columns are the important structural elements to transfer the superstructures load to the foundation. Deterioration and degradation of the structural elements is the critical issue facing by the entire world. Corrosion is one of the most common cause responsible for the deterioration. To maintain structural safety as well as serviceability, it is important to estimate the residual capacity of the deteriorated reinforced concrete columns. Available analytical models are unable to give the desired results, as these models were based on various assumptions and developed with a limited dataset only. These challenging issues are addressed by artificial intelligence based on machine learning techniques. In this work, the neural network-based model is developed to estimate the residual capacity of the corroded RC columns. The proposed model has good accuracy with an R 2-value (the coefficient of determination) of 0.9981 and a mean absolute percentage error of 3.84%. The other performance indices such as mean absolute error, Nash-Sutcliffe effecience index, and a20-index have 10.97 kN, 0.99, and 0.96 values, respectively. The proposed model can be utilized by structural designers and researchers to estimate the residual life of the corroded RC columns.
... The 11 th neuron is ranked 1 st for having the highest Pearson's correlation coefficient and least MSE. After the selection of the best neuron synaptic weights and biases were calculated between the input and hidden layer and the transfer function was applied to the normalized input using equation 4, and the obtained values of Pi were used in equation 2 to calculate the final value of the compressive strength [18,19]. ...
Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete , the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult problems. Artificial intelligence has enough capabilities to deal with such kind of complex problems. In this work, an artificial neural network (ANN) based model has been developed to predict the compressive strength of steel fiber and silica fumes-based concrete. The R-value of the developed model is 0.9948 and the mean absolute percentage error is 5.47%. The mean absolute error and root mean square error of the proposed model is 1.73 MPa and 6.89 MPa, respectively. The developed model is easy to use and reliable to estimate the compressive strength of concrete incorporating silica fumes and steel fibers.
... It can be summarized from previously published studies that the lower values of MAE, MAPE, and RMSE, and higher values of R, Buildings 2023, 13, 931 9 of 32 a20-index, and NS represent positive outcomes of the models. The expressions of statistical indices [90,91] are given below: ...
The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.
... In the case of a perfect model with a zero-estimate error variance, the Nash-Sutclife efciency (NSE) equals one (NSE � 1) and vice versa. Te a-20 index is a recently introduced statistical engineering measure that can be used to evaluate AI models by displaying the number of samples that suit the estimation values with a 20% variance from experimental values [60][61][62]. R 2 , NS, and a-20 index values closer to 1 indicate the best correlation between the estimated and the experimental results. Te lower the values of errors (MAPE, MSE, and RMSE), the better the performance of the model. ...
Corrosion of embedded steel reinforcement is the prime influencing factor that deteriorates the structural performance and reduces the serviceability of reinforced concrete (RC) structures, especially during earthquakes. In structural elements, RC columns play a vital role in transferring the superstructure’s load to the substructure. The deterioration of RC columns can affect the structures’ overall performance. Hence, it becomes essential to estimate the remaining life of deteriorated RC columns. In the literature, only limited analytical models are available to calculate the remaining life of corroded and eccentrically loaded RC columns. As the number of dependent parameters increases, assessing the residual life of the structural elements and providing a practically applicable suitable model become very complex. Machine learning (ML)-based prediction models are beneficial in dealing with such complex databases. In this article, an ML-based artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) algorithms have been applied to estimate the residual strength of corroded and eccentrically loaded RC columns. The performance of the analytical and ML models is accessed using commonly used performance indices, namely, the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), a-20 index, and Nash–Sutcliffe (NS). The results of the proposed ANN model have been compared with the existing analytical model to identify the suitability of the best model. Based on performance analysis, the precision of the GPR and SVM models is lower than that of the ANN model. The processed results revealed that the R2 value of the ANN model for training, testing, and validation datasets is 0.9908, 0.9757, and 0.9855, respectively. The MAPE, MAE, RMSE, NS, and a-20 index for all the datasets are 8.31%, 48.35 kN, 72.53 kN, 0.9886, and 0.8978, respectively. The precision of the ANN model in terms of the coefficient of determination is 225.77% higher than that of the analytical model. The sensitivity analysis demonstrates that the compressive strength of concrete plays the most significant role in the load-carrying capacity of corroded and eccentrically loaded RC columns. The proposed ANN model is reliable, accurate, fast, and cost effective. This model can also be used as a structural health-monitoring tool to detect the early damages in the RC columns.
