FRP-strengthened Reinforced Concrete Research Articles

Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements

This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structures.

Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP

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.

Aggregate interlock and effective strain of FRP-strengthened flanged RC members subjected to torsion: Experimental evaluation and analytical modeling

The effective strain of externally bonded FRP strengthening systems and the loss of concrete aggregate interlock are critical factors for predicting strength and developing design models for structural strengthening. Current research on FRP strengthening of reinforced concrete (RC) members primarily focuses on flexural, shear, and axial loading, neglecting torsional systems. This study introduces an innovative analytical-experimental model/formulation predicting FRP effective strain and concrete strain, based on Digital Image Correlation (DIC) analysis outcomes of an experimental study on RC beams strengthened with FRP sheets under torsion. Eight flanged RC beams, strengthened with various FRP ratios, configurations, and installation methods were also systematically tested. The findings emphasize the need for reevaluation of strain limits in existing guidelines. It points out that the shear integrity of concrete is influenced by concrete compressive strength and strengthening scheme. The DIC results suggest that the strain limit for concrete surfaces between FRP strips should be in the range of 0.0055–0.008, as opposed to the limit of 0.004 set by ACI 440.2R-17. The use of the grooving method in certain systems also requires an increase factor of 1.65 and 1.45 for the effective strain of transverse and longitudinal FRP sheets.

Mesoscale discrete simulation of flexural behavior of FRP-strengthened RC beams using 3D RBSM

A widely adopted method of strengthening existing reinforced concrete (RC) structures, especially for marine concrete structures, is to use external bonded fiber-reinforced polymer (FRP). However, although numerous studies in this field have been reported, accurate simulation of localized material behaviors such as crack opening and interfacial sliding remains challenging. Compared to other simulation methods, mesoscale models offer the promise of capturing these complexities. Nevertheless, there has been very little application of mesoscale simulations in analysis of FRP-strengthened RC structures due to a lack of relevant constitutive models. To fill this gap, the authors have recently developed local bond models for the FRP-concrete interface in the three-dimensional rigid body spring model (3D RBSM). The model’s parameters are defined with easily interpretable physical implications and straightforward computational methods. In this work, this mesoscale model is extended and applied to simulate the flexural behavior of FRP-strengthened RC beams. The simulation results closely align with experiments in both global and local behaviors. The parametric study conducted in this work highlights the critical role of interfacial normal bond strength between FRP and concrete in dictating flexural behavior. The capability of 3D RBSM in simulating the strain distribution along the FRP and capturing intricate localized behaviors aligned with flexural and shear forces is demonstrated. Overall, this paper reinforces the potential of 3D RBSM as a robust tool for analyzing the complex behaviors of FRP-strengthened concrete structures.

ANN based fire resistance prediction of FRP-strengthened RC slabs with fireproof panel including air layer

In this study, an Artificial Neural Network (ANN) model to predict the fire resistance of Fiber Reinforced Plastic (FRP) strengthened Reinforced Concrete (RC) slabs with fireproof panel including air layer exposed to fire is developed. FRP-strengthened RC slabs with fireproof panel including air layer has a structure that improves structural performance and fire resistance by placing FRP and a fireproof panel including air at the bottom of the RC slab. First, fire test is performed using the air layer as a variable, and the validity of the software ABAQUS heat transfer analysis is verified by comparing it with the fire test results. Through this heat transfer analysis, the temperature distribution of the slab is determined, and 1521 data sets are obtained by calculating the strength reduction coefficient and elastic coefficient reduction coefficient of each material. The data consist of eight input parameters: the type of fire curve, fire exposure time, insulation thickness, air layer thickness, concrete covering thickness, and FRP thickness. The output parameter is the value of the strength and elastic modulus reduction coefficient for each material. The average values of MSE, RMSE, R, and SSE for all output parameters, which are performance indicators of the ANN model, are 0.00202, 0.03978, 0.98235, and 1.10445, respectively, and the developed ANN model has high computational accuracy and high generalization ability. Therefore, the ANN model developed here can determine the mechanical properties of each material in FRP-strengthened RC slabs with fireproof panel including air layer exposed to fire and evaluate the fire resistant without complex calculations. Through contribution and sensitivity analysis, the effect of input data on output data is confirmed.

Analytical study on flexural strengthening of narrow RC beams with hybrid anchored CFRP sheets

Premature debonding of fiber-reinforced polymer (FRP) sheets commonly occurs in purely bonded reinforced concrete (RC) beams. A hybrid strengthening method for narrow FRP-strengthened RC beams has been proposed to solve this problem. Experimental results have verified the effectiveness of the novel strengthening system in preventing the end debonding and impeding the intermediate debonding of the FRP sheet. However, the mechanism of its strengthening efficiency has not been studied deeply enough. Therefore, this paper developed a non-linear three-dimensional finite element (FE) model to study the flexural behavior of the FRP-strengthened narrow RC beams hybrid anchored with end self-locking system. The FE model was verified against the available experimental studies and showed good agreement, with a R2 of 0.97. Based on the verified model, parametric studies were performed to study further the influences of four major factors: FRP sheet length, steel rebar ratio, concrete compressive strength, and number of FRP sheet layers. The efficacy of hybrid strengthened beams was reduced with longer FRP bond lengths and higher steel rebar ratios. Conversely, increasing concrete compressive strength and FRP sheet layers to a certain degree enhanced the efficacy. Finally, an analytical model to predict the load-deflection curve of the beam was proposed.

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

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.

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Torsional behavior of FRP-strengthened reinforced concrete members considering various wrapping configurations: Theoretical analysis and modeling

Reinforced concrete (RC) structural members such as spandrel, flanged and curved beams, slab beams, and bridge girders may be subjected to significant torsional loading. Despite extensive research on the strengthening of RC members, very little is known about the torsional behavior of RC members strengthened with fiber-reinforced polymer (FRP) sheets. Therefore, very limited guidelines apply to the design of externally bonded FRP systems for torsional strengthening, and no design provisions exist for the U-wrapped and 2-sided strengthening systems in torsion. This study aims to contribute to this growing area of research by developing a new analytical model to predict the full torsional response of strengthened RC members with many conventional configurations such as spiral, transverse and longitudinal FRP, continuous and intermittent strips along the beam length, side bonded, and full-wrapped systems. Moreover, this study provided an analytical model accurately predicting the contribution, efficiency, and failure mode of side-bonded strengthening systems. Additionally, the influence of various parameters such as FRP stiffness and transverse steel reinforcement ratio is taken into account in the constitutive law of concrete and internal reinforcement for the first time. The good agreement between the test results of 32 collected FRP-strengthened RC beams from the literature and the theoretical ones, which were predicted through programming proves the validity and accuracy of the presented theory.

Effect of wetting/drying cycling on flexural behavior of RC beams strengthened by prestressed CFRP plate: Experimental and analytical investigations

Using prestressed carbon fiber reinforced polymers (CFRPs) is a promising technique for strengthening reinforced concrete (RC) structures. This study aimed to investigate the prestress losses and flexural behavior of strengthened RC beams under wetting/drying cycling (WDC) in a chloride-containing environment. Two prestress levels (20% and 40%) and two WDC durations (90 and 180 cycles) were considered. The time-dependent prestress loss under WDC was first monitored by fiber bragg grating (FBG) for 90 days, and the composition of the prestress loss was theoretically analyzed. A four-point bending test was conducted on the strengthened beams after the WDC exposure, and the intermediate crack (IC) debonding strain and the ultimate moment of the strengthened beams were analytically evaluated. The results show that although WDC exposure aggravated the time-dependent prestress losses, the maximum time-dependent prestress losses after 90 WDCs were only 1.75%, indicating the effectiveness of the end anchorage system. After WDC exposure, the failure mode of the strengthened beams shifted from CFRP rupture to IC debonding, accompanied by the reduction of cracking and the ultimate load, CFRP utilization, and the increase of the maximum crack width. Besides, the model of Lu et al. and Said and Wu could be applied to the IC debonding strain prediction after the WDC. When the environmental reduction coefficient was incorporated, ACI 440.2 R and CECS-146 resulted in a precise IC debonding strain. The proposed method could accurately predict the ultimate moment of the prestressed FRP-strengthened RC beams with different failure modes after WDC.

Experimental and numerical study of PEN FRP-strengthened circular RC columns under lateral cyclic loading with high axial compression ratio

This paper presents experimental studies and numerical simulations of circular reinforced concrete (RC) columns wrapped with Polyethylene Naphthalate fiber reinforced polymer (PEN FRP, a kind of large-rupture-strain FRP) and conventional CFRP. A total of seven columns were designed and fabricated, including a control column, three columns strengthened with CFRP, and three columns strengthened with PEN FRP. All columns were subjected to cyclic lateral load and a constant high axial load. The effectiveness of PEN FRP strengthening was systematically analyzed and discussed regarding the failure mode, displacement ductility, lateral cyclic load–displacement curve, stiffness degradation and energy dissipation capacity. The test results indicated that the control column exhibited inferior ductility and peak lateral strength, while FRP-strengthened columns possess better energy dissipation capacity and ductility. Further study found that PEN FRP was preferred to CFRP with similar tensile stiffness in seismic strengthening. Finally, based on the cyclic compression model of LRS FRP-strengthened concrete, the improved tensile model of FRP-strengthened concrete, and the cyclic stress–strain model of reinforcing steel, numerical simulations for seismic performance were carried out in OpenSees. It was found that the numerical simulation results of all FRP-strengthened RC columns were in excellent agreement with the test results. To better understand the effect of FRP confinement stiffness on the seismic performance of circular columns, parametric analysis was performed. Results showed that the numerical simulations need to consider the buckling of longitudinal reinforcement for accurately predicting the seismic behavior of FRP-strengthened columns of lower confinement stiffness.

Prediction of the Moment Capacity of FRP-Strengthened RC Beams Exposed to Fire Using ANNs

In this study, an Artificial Neural Network (ANN) model to predict the moment capacity of Fiber Reinforced Plastic (FRP) strengthened Reinforced Concrete (RC) beams exposed to fire is developed. The software ABAQUS heat transfer analysis is verified by comparison with the fire resistance test results. Through this heat transfer analysis, the temperature distribution of the beam section is determined, and 400 datasets are obtained using the moment capacity calculation method combined with the section equilibrium method. The data consist of eight input parameters: the beam width, beam height, FRP area, rebar area, concrete compressive strength, insulation thickness, concrete cover depth, and fire exposure time. The output parameter is the moment capacity. The ANN model is developed through a sensitivity study using the algorithm type and the number of hidden-layer neurons as variables. The average error between the predicted data of the developed ANN model and the target data obtained from the moment capacity calculation method was 0.35 kN·m, and the average relative error was 0.2512%, showing high accuracy. Therefore, the ANN model developed here can determine the moment capacity without complex calculations. The effects of the input parameters on the moment capacity of the FRP-strengthened RC beams exposed to fire are investigated using the ANN model.

A mixed finite element model for FRP-strengthened RC slabs based on sequential linear analysis

The aim of the present study is to develop a quasi-3D finite element model for predicting the nonlinear behavior of reinforced concrete (RC) slabs strengthened with fiber-reinforced polymers (FRP). The equivalent layered concept is adopted for modelling the constituent parts of the slab, including steel reinforcements, FRP sheets, concrete, and adhesive layers. The kinematical relation of the FRP-strengthened RC slab is approximated by using a mixed stress-displacement field plate theory. Material nonlinearity of the slab's constituents is included in the formulation. To overcome the convergence problems that traditional nonlinear algorithms are usually encountered with them, the sequential linear analysis approach is employed for simulating the crack propagation process. To evaluate the accuracy, numerical results obtained from the presented sequential linear FE model are compared with other similar numerical and experimental results available in the literature. The comparisons show that the proposed formulation is a simple and efficient tool for predicting the nonlinear behavior of FRP-strengthened RC slabs.

Multiscale viscoelastic analysis of FRP-strengthened concrete beams

A multiscale framework with an elastic reinforced concrete (RC) beam bonded to a fiber reinforced polymer (FRP) plate (connected by an adhesive layer with viscoelastic properties) is established in this research; this framework is proved to be fast and efficient in avoiding time integration and complex meshing in existing FE models. The FRP plate is composed of elastic fibers and surrounded by viscoelastic polymers whose effective properties are generated by the locally exact homogenization theory. According to the correspondence principle, the viscoelasticity problem can be solved in the Laplace domain as an elasticity problem. This leads to solutions that can be transformed back to the time domain through Zakian's numerical method. To validate the present method, comparisons with the simulations reported in literature are presented. The effects of cracks, prestress in FRP plates, microscale fiber content, and temperature on the long-term behavior of FRP-strengthened RC beams are effectively investigated using the present method. Through the recovery of stress relaxation within the adhesive layer, the present technique is found capable of conveniently predicting the long-term behavior of FRP-strengthened RC beams, providing a robust tool for structural design and maintenance.

Machine learning-based model for recognizing the failure modes of FRP-strengthened RC beams in flexure

Plate end (PE) debonding and intermediate crack (IC) debonding are the main failure modes of fiber reinforced polymer (FRP) strengthened reinforced concrete (RC) beams in flexure. Different failure modes exhibit different failure characteristics. Therefore, accurately identifying the failure mode is of great significance in selecting methods to prevent the debonding of the strengthened beams. This study first established a primary indicator system through literature research. Then one hundred and eighty-eight FRP-strengthened RC beams containing PE and IC debonding were collected from the published literature of forty-eight researchers. After that, correlation analysis and grey correlation analysis were used to study the data of two failure modes. Finally, the indicator system for the prediction of failure modes was established. After the indicator system was established, six machine learning algorithms, including K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF), back propagation neural network (BPNN), logistic regression (LR), and support vector machine (SVM), were used to build the prediction model of failure modes. The evaluation of the models shows that the coefficient of variation of the accuracy of the decision tree is merely 5.4%, which has the best robustness; the average accuracy of the random forest for the two failure modes reaches 93% and 98%, which has the highest precision.

Ultimate torsional resistance and failure modes of FRP-strengthened reinforced concrete members: A nonlinear design model

Reinforced concrete (RC) structural members, especially flanged and spandrel beams, bridge girders, curved beams, and eccentrically loaded beams may be subjected to significant torsional loading. Despite extensive research on the strengthening of RC members, far too little attention has been paid to the torsional behavior of RC members strengthened with fiber-reinforced polymer (FRP) sheets. Therefore, a limited number of guidelines specified the design of externally bonded FRP systems in torsion. This study provides an important opportunity to advance the understanding of the torsional behavior of FRP-strengthened members by developing a nonlinear design model predicting the failure mode and ultimate torsional resistance of FRP-strengthened RC members subjected to torsion. Serious discussions and analyses of desired failure modes emerge by defining two balanced conditions between the components, and eight failure regions. The first balanced condition defined between FRP and concrete specifies the failure mode of the member, and the second balanced condition defined between steel and concrete is used for the analysis of steel reinforcement stresses. A clear design approach is proposed in this study to access the desired percentage of the externally applied FRP sheets based on the required torsional strength. Moreover, the ultimate stresses and strains of concrete struts, steel reinforcements, and FRP sheets are derived by incorporating the nonlinear behavior of concrete. To evaluate the validity of the design method, 70 FRP-strengthened specimens are collected from 11 previous studies; then, a comparative analysis is conducted among the experimental results, the estimations of the presented model, and the predictions of the commonly available guidelines for torsional strengthening. The excellent agreement between the test results and the presented model estimations confirms the validity and accuracy of the presented model.

Effect of corroded reinforcement on capacity of square reinforced concrete columns confined with FRP sheets under eccentric loads

Steel corrosion is one of the primary causes which significantly degrade the load-carrying capacity and FRP (fibre reinforced polymer) strengthening efficiency of FRP-strengthened reinforced concrete columns under eccentric loading. However, this issue has not been studied comprehensively and systematically in the literature. Particularly, there has been no study on the strengthening efficiency of basalt FRP (BFRP) which is becoming popular due to its excellent performance and affordable price. This study experimentally investigates the effect of corrosion level of reinforcement by mass (0%, 15% and 30%), relative eccentricity (e/h = 0.125 and 0.375), type of FRP (CFRP and BFRP) and FRP thickness (1 or 3 layers) on the load-carrying capacity and deformation of corroded-reinforcement reinforced concrete (RC) columns. The experimental results showed that steel corrosion reduced the load-carrying capacity of the unstrengthened RC columns up to 23% and this degradation increased with the corrosion level. Both CFRP and BFRP have proven excellent strengthening efficiency since the capacity of the FRP-strengthened corroded-reinforcement RC columns increased up to 47%. Particularly, the strengthening efficiency of BFRP sheets was slightly lower than that of CFRP sheets (<7%). On the other hand, the increase of the relative eccentricity from e/h = 0.125 to e/h = 0.375 reduced the strengthening efficiency of FRP. In addition, a semi-empirical model was proposed to predict the capacity of corroded-reinforcement RC columns strengthened with FRP under eccentric loading. The predictions match well with the experimental results with a small coefficient of variation.

Flexural Performance and End Debonding Prediction of NSM Carbon FRP-Strengthened Reinforced Concrete Beams under Different Service Temperatures

This paper aims to evaluate the influence of relatively high service temperatures (near or beyond the glass transition temperature (Tg) of epoxy adhesive) on the flexural performance and end debonding phenomenon in near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP)-strengthened, reinforced concrete (RC) beams. To this end, an experimental program consisting of 24 beams (divided into four groups) was performed, where different parameters was combined (i.e., service temperature, steel reinforcement ratio, CFRP ratio, and concrete compressive strength). In addition, the effect of the testing temperature on the end debonding phenomenon was investigated with an analytical procedure according to fib Bulletin 90, and the predictions were compared to experimental results. Taking specimens tested at 20 °C as a reference, no considerable change was observed in the ultimate load of the specimens tested below 60 °C (being in the range of epoxy Tg), and all specimens failed by FRP rupture. On the other hand, the increase in testing temperature up to 70 and 85 °C was followed by a decrease in the capacity of the strengthened beams and a change in failure mode, moving from FRP rupture to end debonding and concrete crushing. The analytical procedure successfully predicted the occurrence of premature end debonding failure and demonstrated that the effect of temperature on the mechanical properties of materials can be a key factor when predicting the premature end debonding in a NSM joint.

Fire Resistance Evaluation of Concrete Beams and Slabs Incorporating Natural Fiber-Reinforced Polymers.

This paper presents a numerical study to evaluate the fire resistance of concrete beams and slabs incorporating natural fiber-reinforced polymers (FRP). A validated finite element model was applied to carry out a series of numerical studies on fire-exposed reinforced concrete (RC) beams and slabs strengthened with conventional and bio-based FRP composites. The model calculates the temperature-dependent moment-curvature relationship for various segments of the member at each time step, which are then used to calculate the moment capacity and deflection of the member. The variables in the beams and slabs include different strengthening techniques (externally bonded FRP and near-surface mounted FRP), different fiber composites, and fire insulation schemes (uninsulated and insulated). The results from the study indicate that the bio-based FRP-strengthened RC members undergo a faster degradation in moment capacity and also experience higher deflections under fire exposure. This leads to a lower fire resistance in RC members with bio-based FRP composites compared to beams and slabs with conventional FRP-strengthened concrete members. The addition of fire insulation to the bio-based FRP-strengthened members can enhance their fire performance and help achieve the required fire resistance ratings for use in building applications. In this study, the NSM CFRP-strengthened RC beams were found to have a fire resistance of 3 h without any fire insulation; however, the bio-based FRP-strengthened beams required a layer of vermiculite-gypsum-based fire insulation material (of about 25 mm) to achieve similar fire resistance.

Novel Embedded FRP Anchor for RC Beams Strengthened in Flexure with NSM FRP Bars: Concept and Behavior

The near-surface-mounted (NSM) fiber-reinforced polymer (FRP) strengthening technique for reinforced-concrete (RC) beams has attracted worldwide research attention and application in the past two decades. In spite of improved bond strength between the NSM FRP and concrete, compared with the externally bonded (EB) FRP method, premature debonding failure still occurs, and, hence, the efficiency of the NSM FRP intervention is limited. An intuitive means to enhance efficiency is to apply anchorage devices to the RC beam; however, the availability of viable anchorage is limited for NSM applications. A novel anchorage device is therefore presented in this paper, which is referred to as an embedded FRP anchor (EFA). The concept, manufacture, and installation of the EFAs were initially presented. The effectiveness of EFAs in NSM FRP-strengthened RC beams was then ascertained through full-scale beam tests and comparison with an NSM FRP-strengthened beam anchored with a U-jacket. Moreover, the effects of EFA parameters on the behavior of anchored beam were also investigated. The test results not only prove the high anchoring efficiency of EFA but also reveal the significant effects of EFA parameters on the failure mode, load–deflection response, and NSM FRP strain of the EFA anchored beam.