FRP-reinforced Concrete Slabs Research Articles

Enhancing machine learning models with metaheuristic Optimization techniques for accurate prediction of PSC in FRP-reinforced concrete slabs

The present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predicting the capacity for punching shear of Fiber Reinforced Polymer (FRP) reinforced concrete slabs in the absence of shear reinforcement. A database including 610 experimental results is constructed, and the efficiency of twenty metaheuristic algorithms is assessed in optimizing the hyperparameters of the XGBoost model. The enhanced models undergo evaluation and comparison with existing prediction algorithms, showcasing their better accuracy. The study reveals that the DE, MVO, EOA, and VCS algorithms yield the highest precision among the investigated optimization techniques out of which VCS model performed better. Furthermore, the significance of input parameters on the punching shear capacity is analyzed by implementing the Shapley Additive Explanation (SHAP) method. The findings emphasize the potential of metaheuristic optimization in enhancing the estimating abilities of machine learning models for complex engineering problems and provide valuable insights into the key factors influencing the punching shear strength of concrete slabs reinforced with FRP.

Machine-Learning-Based Predictive Models for Punching Shear Strength of FRP-Reinforced Concrete Slabs: A Comparative Study

This study is focused on the punching strength of fiber-reinforced polymer (FRP) concrete slabs. The mechanical properties of reinforced concrete slabs are often constrained by their punching shear strength at the column connection regions. Researchers have explored the use of fiber-reinforced polymer reinforcement as an alternative to traditional steel reinforcement to address this limitation. However, current codes poorly calculate the punching shear strength of FRP-reinforced concrete slabs. The aim of this study was to create a robust model that can accurately predict its punching shear strength, thus improving the analysis and design of composite structures with FRP-reinforced concrete slabs. In this study, 189 sets of experimental data were collected, and six machine learning models, including linear regression, support vector machine, BP neural network, decision tree, random forest, and eXtreme Gradient Boosting, were constructed and evaluated based on goodness of fit, standard deviation, and root-mean-square error in order to select the most suitable model for this study. The optimal model obtained was compared with the models proposed by codes and the researchers. Finally, a model explainability study was conducted using SHapley Additive exPlanations (SHAP). The results showed that random forests performed best among all machine learning models and outperformed existing models suggested by codes and researchers. The effective depth of the FRP-reinforced concrete slabs was the most important and proportional to the punching shear strength. This study not only provides guidance on the design of FRP-reinforced concrete slabs but also informs future engineering practice.

Machine Learning-Based Prediction Models for Punching Shear Strength of Fiber-Reinforced Polymer Reinforced Concrete Slabs Using a Gradient-Boosted Regression Tree.

Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R2) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements.

An Experimental Investigation of Flexural Performance of FRP Reinforced Concrete Slabs

An Experimental Investigation of Flexural Performance of FRP Reinforced Concrete Slabs

Behavior of one-way steel, BFRP, and GFRP reinforced concrete slabs under monotonic and cyclic loadings: Experiments and analyses

This paper experimentally investigated the monotonic and cyclic behaviour of one-way basalt and glass fibre-reinforced polymers (BFRP and GFRP) reinforced concrete (RC) slabs compared with that of traditional steel RC ones. A total of nine 2000×700×100 mm RC slabs, including three BFRP RC (BRC), three GFRP RC (GRC), and three steel RC (SRC) slabs, were tested. The results indicated that the behaviour of BRC and GRC slabs was governed by the elastic characteristic of FRP bars, resulting in a more uniform curvature of the slabs. The behaviour and failure modes of BRC and GRC slabs were comparable with those of SRC slabs. The similarity and difference were characterized by the rupture of FRP bars and the yielding of steel bars. Postcrack stiffness of BRC and GRC slabs was significantly lower than that of SRC slabs and was marginally affected by the effect of cyclic loading. The ultimate loads and ultimate deflections of BRC and GRC slabs were ∼2.0 and 1.6–2.0 times those of SRC slabs, respectively. Concepts of ductility, safety factor, and warning index were introduced and quantified for FRP RC slabs. BRC and GRC slabs had a significantly higher ductility (8.5–10.4) than SRC slabs (5.1–6.2), showing a highly ductile behaviour. SRC slabs had a safety factor of 1.0–1.1, while BRC and GRC slabs had a significantly higher safety factor of 3.8, showing their lower failure probability of BRC and GRC slabs when they are designed at a similar load as SRC slabs. The warning index of SRC slabs was 14.4–30.2, and that of BRC and GRC slabs was 65.1–71.6 and 63.9–86.0, respectively. The strength, ductility, safety factor, recovery, and warning index of BRC and GRC slabs were high, confirming their advanced characteristics. Under cyclic loading, BRC and GRC slabs have significantly larger stiffness degradations (4.4 %–7.2 %/cycle) than SRC slabs (1.62 %−3.2 %/cycle). The results of theoretical analyses showed that the strengths of BRC and GRC slabs can be reasonably predicted. The outcomes indicate the high potential use of FRP reinforcement for slabs in engineering practice.

Shear and flexure of FRP-reinforced concrete beams and slabs – A review

The article reviews structural response of flexural members reinforced with fiber-reinforced polymer (FRP) as a sustainable alternative to conventional carbon steel. It overviews experimental studies and code-based models that characterize shear and flexural response of concrete beams and slabs reinforced with FRP bars. Discussion on lower resistance to fire being key limitation of FRP bars is also included. The most commonly used fibers to manufacture FRP bars include carbon, glass, basalt, aramid, or combinations of these materials. Consequently, each material plays its role in contributing to shear and flexure differently. Basalt FRP are characterized by higher tensile capacity and resistance to corrosion, carbon FRP bars can withstand exposure to harsh environments; glass FRP bars possess lower modulus of elasticity that may cause large deflection and wider cracks. FRP bars are generally preferred over carbon steel for use in concrete elements where exposure to chlorides or chemicals is expected, or places where non-magnetic properties are essential. Beams and slabs reinforced with FRP bars are likely to undergo larger deflections due to lower stiffness of FRP bars. The dominant failure mode of FRP-reinforced concrete flexural members is discussed in comparison to conventional steel-reinforced members. The article identifies increasing interest and research in using green concrete reinforced with FRP bars that helps to make construction serviceable and sustainable. However, premature de-bonding of bars remains a concern with more research needed to avoid deterioration of mechanical properties by underutilization of materials. Although concrete is practically fire-resistant, FRP is likely to undergo damage of resin due to the presence of combustible organic polymers. The bulk of research implies that the thermal instability of the resin in FRP reinforcing bars is not a disadvantage that outweighs the possible benefits of its usage in the construction industry.

Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs

Fiber reinforced polymer (FRP)-reinforced concrete slabs, an extension of reinforced concrete (RC) slabs leveraged for resisting environment corrosion, are susceptible to punching shear failure due to the lower elasticity modulus of FRP reinforcement. To estimate the punching shear resistance accurately, there are two types of models (e.g., white box and black-box models) proposed based on theoretical derivations and machine learning methods. However, these two types of models are considered as independent of each other. In this study, a hybrid model (e.g., grey-box model) derived from modified compression field theory (MCFT) is proposed by this paper, in which the performance is improved by a machine-learning-aided approach (genetic programming). In order to exploit the performance of machine learning, a database containing 154 experimental data is established and used for fitting the correction equations. Iterating the population containing 300 tree-based individuals in 300 times, a correction equation with simple format is obtained, which performs well in performance improvement of the basic model derived from MCFT. Herein, the influential factors involved in the correction equation comply with the sorting in order of the importance quantified by extreme gradient boosting (XGBoost) and shapley additive explanation (SHAP). Combining the correction equation with the basic model derived from MCFT, a symbolic regression MCFT (SR-MCFT) model is established, which performs better prediction performance than other five empirical models.

Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an Optimization Algorithm.

FRP (fiber-reinforced polymer)-reinforced concrete members have larger deflection than reinforced concrete members because of the low modulus of elasticity of the FRP bar. In this paper, we proposed a new effective moment of inertia equation to predict the deflection of FRP-reinforced concrete members based on the harmony search algorithm. The harmony search algorithm is used to optimize a function that minimizes the error between the deflection value of the experimental result and the deflection value expected from the specimen's specifications. In the experimental part, four GFRP (Glass Fiber-Reinforced Polymer)- and BFRP (Basalt Fiber-Reinforced Polymer)-reinforced concrete slab specimens were manufactured and tested. FRP-reinforced concrete slabs were reinforced with GFRP and BFRP rebars on spiral rib surfaces. The effects of the FRP reinforcement ratio and balanced reinforcement ratio (ρf/ρfb), the moment of inertia of the transformed cracked section and the gross moment of inertia (Icr/Ig), and the cracking moment and the maximum service load moment (Mcr/Ma) on the effective moment of inertia have been considered. The experimental results and predicted results of the flexural testing of concrete slabs reinforced with FRP rebars were compared, and the experimental results were in good agreement with the calculated values using the proposed effective moment of inertia equation.

Development of machine learning models for reliable prediction of the punching shear strength of FRP-reinforced concrete slabs without shear reinforcements

This study aims to examine the punching shear strength of FRP-reinforced concrete slabs, which is a complex behavior affected by several mechanisms and many variables. In this study, assessment of selected strength models available in the literature using a simplified reliability analysis method showed the need for more accurate and consistent strength models. Thus, two machine learning (ML) models were developed and proposed. Both models accurately predict the strength compared to the available models, which provides an alternative method to the available ones. In addition, the effect of the main variables on the strength using the proposed models was compared to that using the available models, which provided an insight on the impact and interrelationship of effective variables on such a complex problem. Such insight can be helpful in future development of design codes.

Punching Shear Strength of FRP-Reinforced Concrete Slabs without Shear Reinforcements: A Reliability Assessment.

The recent failure of buildings because of punching shear has alerted researchers to assess the reliability of the punching shear design models. However, most of the current research studies focus on model uncertainty compared to experimentally measured strength, while very limited studies consider the variability of the basic variables included in the model and the experimental measurements. This paper discusses the reliability of FRP-reinforced concrete slabs’ existing punching shear models. First, more than 180 specimens were gathered. Second, available design codes and simplified models were selected and used in the calculation. Third, several reliability methods were conducted; therefore, three methods were implemented, including the mean-value first-order second moment (MVFOSM) method, the first-order second moment (FOSM) method, and the second-order reliability method (SORM). A comparison between the three methods showed that the reliability index calculated using the FOSM is quite similar to that using SORM. However, FOSM is simpler than SORM. Finally, the reliability and sensitivity of the existing strength models were assessed. At the same design point, the reliability index varied significantly. For example, the most reliable was the JSCE, with a reliability index value of 4.78, while the Elgendy-a was the least reliable, with a reliability index of 1.03. The model accuracy is the most significant parameter compared to other parameters, where the sensitivity factor varied between 67% and 80%. On the other hand, the column dimension and flexure reinforcement are the least significant parameters compared to other parameters where the sensitivity factor was 0.4% and 0.3%, respectively.

Punching shear strength and deformation for FRP-reinforced concrete slabs without shear reinforcements

While very little is known about the punching shear behavior of Fiber Reinforced Polymer (FRP)-reinforced concrete slabs; thus, very few design codes for this case are available. Most available design codes and guidelines are missing the physical meaning of various aspects. In addition, the critical shear crack theory (CSCT), developed in the '90s for shear and punching shear of concrete elements, has covered a wide range of shear and punching shear problems but did not tackle punching shear of FRP-reinforced concrete slabs. The strength of FRP-reinforced concrete slabs is different from the conventional steel-reinforced concrete slabs in several ways, including but not limited to: (1) having a variable young’s modulus depending on the type of FRP; (2) the flexure strength of FRP-reinforced concrete slabs is different, while the failure occurs due to concrete crushing; and (3) FRP is linear up to failure with no yield plateau. This study aims to develop a mechanical model based on FRP-reinforced concrete slabs' physical punching shear behavior. An extensive experimental database was collected with 189 FRP-reinforced concrete slabs from 37 different studies. A new mechanical model was developed and proposed based on the critical shear crack theory (CSCT). The proposed model captured the behavior accurately and was based on a physical model. The proposed model included the effect of young’s modulus and the different failure modes. It can predict both the strength and rotation of FRP-reinforced concrete slabs under punching shear.

Critical shear crack theory-based punching shear model for FRP-reinforced concrete slabs

Owing to their high strength-to-weight ratio, superior corrosion resistance, and convenience in manufacture, fiber-reinforced polymer (FRP) bars can be used as a good alternative to steel bars to solve the durability issue in reinforced concrete (RC) structures, especially for seawater sea-sand concrete. In this paper, a theoretical model for predicting the punching shear strength of FRP-RC slabs is developed. In this model, the punching shear strength is determined by the intersection of capacity and demanding curve of FRP-RC slabs. The capacity curve is employed based on critical shear crack theory, while the demand curve is derived with the help of a simplified tri-linear moment-curvature relationship. After the validity of the proposed model is verified with experimental data collected from the literature, the effects of concrete strength, loading area, FRP reinforcement ratio, and effective depth of concrete slabs are evaluated quantitatively.

Prediction of Punching Shear Capacity of Two-Ways FRP Reinforced Concrete Slabs

An innovative solution to the corrosion problem is the use of fiber-reinforced polymer (FRP) as an alternative reinforcing material in concrete structures. In addition to the non corrodible nature of FRP materials, they also have a high strength-to-weight ratio that makes them attractive as reinforcement for concrete structures. Extensive research programs have been carried out to investigate the flexural behavior of concrete members reinforced with FRP reinforcement. On the other hand, the shear behavior of concrete members, especially punching shear of two-way slabs, reinforced with FRP bars has not yet been fully explored. The existing provisions for punching of slabs in most international design standards for reinforced concrete are based on tests of steel reinforced slabs. The elastic stiffness and bonding characteristics of FRP reinforcement are sufficiently different from those of steel to affect punching strength. In the present study, the equations of existing design standards for shear capacity of FRP reinforced concrete beams have been evaluated using the large database collected. The experimental punching shear strengths were compared with the available theoretical predictions, including the CSA S806 (CSA 2012), ACI-440.1R-15 (ACI 2015), BS 8110 (BSI 1997), JSCE (1997) a number of models proposed by some researchers in the literature. The existing design methods for FRP reinforced concrete slabs give conservative predictions for the specimens in the database. This paper also presents a simple yet improved model to calculate the punching shear capacity of FRPreinforced concrete slabs. The proposed model provides the accurate results in calculating the punching shear strengths of FRP-reinforced concrete slender slabs.

Punching Shear Resistance of High Strength GFRP Reinforced Concrete Flat Slabs

This study program has been arranged to test the behavior of punching shear for concrete slabs reinforced by an embedded glass fiber reinforced polymer (GFRP) reinforcements. However, the shear resistance of concrete members in general and especially punching shear of two-way RC slabs, reinforced by GFRP bars has not yet been fully investigated. Seven decades ago, many researches have been carried out on punching shear resistance of slabs reinforced by conventional steel and several design methods were created. However, these methods can be not easily applied to FRP-reinforced concrete slabs due to the difference in mechanical properties between (FRP) and steel reinforcement. sixteen specimens are to be cast in lab within two categories of reinforcements such as GFRP and equivalent steel reinforcements. In addition, based on experimental data obtained from the author’s study and ACI model, the paper performed an evaluation of accuracy of proposed model. The results from the evaluation show that the ACI-formula gave inaccurate results with a large scatter in comparison with the test results of this study. A new design formula can be proposed for more accurate estimation of punching shear resistance of (GFRP) specimens.

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Fibre-reinforced polymer (FRP) provides an alternative reinforcement for concrete flat slabs. This research proposes a hybrid machine learning model for predicting the ultimate punching shear capacity of FRP-reinforced slabs. The model employs the least squares support vector machine (LS-SVM) to discover the mapping between the influencing factors and the slab punching capacity. Furthermore, the firefly algorithm (FA), a population-based metaheuristic, is utilised to facilitate the LS-SVM training. A data-set which contains actual tests of FRP-reinforced concrete slabs is utilised to construct and verify the proposed approach. The contribution of this research is to establish a hybrid machine method, based on the LS-SVM and FA algorithms, for meliorating the prediction accuracy of FRP-reinforced slabs’ ultimate punching shear capacity. Experimental results demonstrate that the new model has achieved roughly 55 and 15% reductions in terms of Root Mean Squared Error compared with the formula-based and Artificial Neural Network methods, respectively.

Shear behavior of post-tensioned FRP-reinforced concrete slabs under static and fatigue loading

A reinforcement system for short span concrete slab bridges combining post-tensioned carbon FRP (CFRP) tendons with non-prestressed glass FRP (GFRP) reinforcement can limit the stresses in the GFRP bars to significantly improve fatigue performance. Despite this, the failure mode may be governed by shear and conventional stirrups are, in general, not practical for the construction of slab bridges; therefore the use of innovative shear reinforcement methods is required. Twenty-five FRP-post-tensioned slabs with various types of FRP shear reinforcement were tested under static or fatigue loading. The main variables included the size, shape and type of FRP shear reinforcement used. Fatigue shear strengths at one million cycles were typically in the range of 50–60% of the ultimate static load capacity. Double-headed shear bars were found to be an excellent solution for the shear reinforcement of shallow concrete members which were easy to install and provided high load capacities and good fatigue performance. The fatigue shear strengths of members with anchored shear reinforcement were conservatively predicted using the simplified modified compression field theory, accounting for the change in concrete tensile strength under fatigue loading.

Behavior of FRP Bars-Reinforced Concrete Slabs under Temperature and Sustained Load Effects

The large temperature variation has a harmful effect on concrete structures reinforced with fiber reinforced polymer (FRP) bars. This is due to the significant difference between transverse coefficient of thermal expansion of these bars and that of the hardened concrete. This difference generates a radial pressure at the FRP bar/concrete interface, and may cause splitting cracks within concrete. This paper presents results of an experimental and analytical study carried out on FRP-reinforced concrete slabs subjected, simultaneously, to thermal and mechanical loads. The analytical model based on the theory of linear elasticity consists to evaluate combined effects of thermal and mechanical loads on the transverse expansion of FRP bars. Parameters studied in this investigation are the concrete cover thickness, FRP bar diameter, and the temperature variation. The thermal cycles were varied from −30 to +60 °C. Comparisons between analytical and experimental results show that transverse strains predicted from the proposed model are in good correlation with experimental results.

Numerical Study of FRP Reinforced Concrete Slabs at Elevated Temperature

One-way glass fibre reinforced polymer (GFRP) reinforced concrete slabs at elevated temperatures are investigated through numerical modeling. Serviceability and strength requirements of ACI-440.1R are considered for the design of the slabs. Diagrams to determine fire endurance of slabs by employing “strength domain” failure criterion are presented. Comparisons between the existing “temperature domain” method with the more representative “strength domain” method show that the “temperature domain” method is conservative. Additionally, a method to increase the fire endurance of slabs by placing FRP reinforcement in two layers is investigated numerically. The amount of fire endurance gained by placing FRP in two layers increases as the thickness of slab increases.

Estimation of the crack width and deformation of FRP-reinforced concrete flexural members with and without transverse shear reinforcement

As the general acceptance of FRP reinforcing bars in the concrete construction industry continues to rise, it is increasingly imperative that the structural behavior of FRP-reinforced concrete members be predicted with good accuracy. The current crack width equation used in North American design codes for FRP-reinforced concrete members assumes a constant value of the bond coefficient which results in inconsistent accuracy of crack width predictions depending on the stress level in the reinforcement relative to the stress at which the bond coefficient was calibrated. Meanwhile, effective moment of inertia equations which have been shown to be reasonably accurate for FRP-reinforced concrete members with transverse shear reinforcement do not account for the additional deformation which may result from the vertical displacement of inclined cracks in members without shear reinforcement. Simple modifications are proposed to the current deflection and crack width equations to improve their accuracy at all reinforcement stress levels within the service range and are compared to the experimental results of four full-scale FRP-reinforced concrete slabs tested in flexure.