Prediction Of Bond Strength Research Articles

Prediction of bond strength between fibers and the matrix in UHPC utilizing machine learning and experimental data

The bond strength between fibers and the matrix plays a crucial role in the tensile strength and post-cracking performance of ultra-high-performance concrete (UHPC). However, existing studies have not attempted to utilize machine learning models for the prediction of bond strength. In this study, machine learning techniques were employed to predict the bond strength of fibers in UHPC. A dataset comprising 658 experimental records was compiled and advanced unsupervised Isolation Forest techniques were utilized to identify and remove outliers, thereby ensuring the accuracy and reliability of the data. Six machine learning models, including ANN, GBDT and XGBoost are utilized in this study, with a focus on evaluating their performance in predicting the maximum pull-out force of fibers. The results indicate that the XGBoost model exhibits exceptional predictive performance, achieving R² value of 0.98, which demonstrates the model's capability to accurately predict the fiber pull-out process. Furthermore, feature importance analysis, visualized through advanced techniques, reveals the significant influence of fiber tensile strength on the pull-out force. A new equation is proposed to predict the maximum pull-out force of fibers and correction factors for different fiber shapes are introduced, significantly enhancing the precision of the calculations. The newly proposed predictive equation has R² value of 0.72, which increases to 0.74, 0.77, and 0.86 after the introduction of shape correction factors, significantly enhancing the accuracy of the computed results. This study not only provides an efficient and reliable method for predicting fiber bond strength in UHPC but also offers an innovative tool for calculating fiber pull-out force.

Machine learning prediction of interfacial bond strength of FRP bars with different surface characteristics to concrete

Fiber-reinforced polymer (FRP) bars have been implemented in civil infrastructures as internal reinforcement. The bond strength of FRP bars to concrete depends on the surface characteristics considerably. This study used machine learning (ML) techniques to explore the influence of bar surface types on the bond properties quantitatively. A database including 158 FRP bars-concrete pull-out testing results was compiled. The geometric factors, including rib spacing (wc), rib width (wf) and rib height (rh), were proposed to quantify the surface configurations of FRP bars. Twelve ML models were trained to predict the interfacial bond strength. The ML models demonstrated higher accuracy in predicting the bond strength compared to eight existing equations from the literature. Among these models, CatBoost exhibited the highest accuracy, with an RMSE 58.3 % lower than the most accurate existing equation. CatBoost was utilized in parametric research on the influencing factors, and demonstrated that wf had the highest weight contribution to interfacial bond strength. Additionally, this study effectively combines ML models with physical meaning-driven analysis methods, resulting in a practical and interpretable equation for calculating FRP bars-concrete interfacial bond strength.

Experimental and theoretical investigation on bond performance between surface-modified bamboo scrimber bars and concrete

With the aim of facilitating the adoption of eco-friendly bamboo scrimber bars as an alternative to the widely used steel bars in concrete structures, center pull-out tests were conducted on 75 specimens to investigate the bond performance between bamboo scrimber bars and concrete. Two innovative modification methods were employed, including the sand coating modification method with varying particle sizes and a newly proposed method involving wrapping with basalt fiber bundles, with a focus on the impact of bundles spacing on bond strength. Additionally, other influence factors, including the concrete compressive strength, development length, and equivalent diameter of bamboo scrimber bars, were investigated. The test results show that compared to unmodified bamboo scrimber bars, those modified with sand and basalt fiber bundles show increases in bond strength of 5.50–5.96 times and 4.82–6.15 times, respectively. The bond strength was most significantly influenced by the development length, which decreases with increasing development length. Besides, specimens with development length less than 75 mm mainly exhibit pull-out failure, while those with development length equal to 75 mm primarily occur concrete splitting failure. Furthermore, a bond strength prediction formula and a bond stress-slip constitutive model for the interface between bamboo scrimber bars modified with sand coating and concrete were established, which show great agreement with the test results.

An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete

Abstract The corrosion behavior of the reinforced concrete structure depends heavily on the interfacial bond behavior between steel and concrete. Over the years, the deterioration of integrated reinforced steel has weakened this bond and potentially led to structural problems. Conventional methods of bond strength evaluation, such as pullout and bond beam tests, is frequently intrusive and tedious. Therefore, there is a growing need for non-intrusive, effective, and reliable forecasting algorithms capable of assessing bond deterioration caused by corrosion. Traditional algorithms for predicting bond strength make it difficult to capture the complex nature of steel-concrete bonds. The present study proposes two different deep learning algorithms, i.e., convolutional neural networks (CNN) and long short-term memory (LSTM), for predicting maximum bond strength in the presence of corrosion. The predictive model is based on a comprehensive dataset comprising 218 datasets from previous studies encompassing diverse input and output variables for predicting the models. The models were trained and tested using the given data to improve early predictions of corrosion-induced bond degradations. The predictive model’s effectiveness was assessed by applying various performance metrics. From this study, the CNN model exhibits higher accuracy and efficiency with mean absolute error, root mean square error, and mean absolute percentage error of 0.25, 0.28, and 95.72, respectively, for predicting ultimate bond strength estimations. The findings of this study provide an accurate and robust prediction model to improve the reliability and safety of the concrete structure by enhancing the residual load-bearing capacity of the concrete structure that has undergone corrosion.

Shear Bond Strength in Stone-Clad Façades: Effect of Polypropylene Fibers, Curing, and Mechanical Anchorage.

This study investigates the shear bond strength between four widely used façade stones-travertine, granite, marble, and crystalline marble-and concrete substrates, with a particular focus on the role of polypropylene fibers in adhesive mortars. The research evaluates the effects of curing duration, fiber dosage, and mechanical anchorage on bond strength. Results demonstrate that Z-type anchorage provided the highest bond strength, followed by butterfly-type and wire tie systems. Extended curing had a significant impact on bond strength for specimens without anchorage, particularly for travertine. The incorporation of polypropylene fibers at 0.2% volume in adhesive mortar yielded the strongest bond, although lower and higher dosages also positively impacted the bonding. Furthermore, the study introduces a novel fuzzy logic model using the Dombi family of t-norms, which outperformed linear regression in predicting bond strength, achieving an R2 of up to 0.9584. This research emphasizes the importance of optimizing fiber dosage in adhesive mortars. It proposes an advanced predictive model that could enhance the design and safety of stone-clad façades, offering valuable insights for future applications in construction materials.

Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications.

A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability

Application of machine learning (ML) in predicting mortars bond strength contributes to low experimental cost and high accuracy. This study explores the performance of four ML models—Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—using 304 experimental data points for training and 76 results for testing. Among these, the XGBoost model demonstrated the highest predictive accuracy, with R², MAE, and RMSE values of 0.95, 0.06, and 0.13 in the training set, and 0.94, 0.08, and 0.16 in the testing set, respectively. Compared with traditional models, ML approaches, particularly XGBoost, were more effective in providing reliable predictions of bond strength. SHapley Additive exPlanation (SHAP) show that substrate type, ethylene-vinyl acetate (EVA) content and water to binder ratio W/C have the most remarkable effect on bond strength, while the type of cement and admixture shows the least effect. These findings highlight the potential of ML models in optimizing mortar formulations and improving prediction reliability.

Machine learning prediction method for the interface bond strength between fiber reinforced polymer bars and concrete based on multi-feature driven analysis

In severe service conditions, corrosion of reinforcement poses a significant challenge for concrete structures. Fiber reinforced polymer (FRP) materials, characterized by their low weight, high strength, and superior corrosion resistance, are effective in enhancing the durability of these structures. The interfacial bond performance between FRP bars and concrete is crucial for their synergistic action and is pivotal for the design and safety evaluation of FRP-reinforced concrete structures. This paper conducts a comprehensive analysis of factors including the material and mechanical properties of FRP bars, concrete mechanical properties, and bond length. Utilizing an exhaustive search strategy, it identifies the optimal characteristic parameters for assessing the bond strength between FRP bars and concrete. Subsequently, these parameters are integrated with four machine learning (ML) algorithms—DT, SVM, RF and XGB—to train the nonlinear mapping relationship between influencing factors and bond strength, proposing a unified and interpretable prediction method for the FRP bar-concrete interface bond strength. The model's predictive accuracy is then validated using a test dataset, and a comparative analysis is performed among the predictive models generated by the four ML algorithms, as well as against empirical calculation formulas. The findings indicate that the ML models offer superior predictive performance and accuracy over empirical formulas, with the XGB algorithm demonstrating the highest overall accuracy and best performance. Moreover, to address the black-box nature of ML algorithms, the SHAP method is employed to enhance the interpretability of the bond strength prediction process. The newly developed hybrid ML model holds promise as a novel approach for accurately assessing the bond strength at the FRP bar-concrete interface.

Natural language processing‐based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete

AbstractAs conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.

Elevated Temperature Effects on FRP–Concrete Bond Behavior: A Comprehensive Review and Machine Learning-Based Bond Strength Prediction

Because of their improved properties, FRP composites are vastly used in the strengthening of aged concrete infrastructures. However, it has been observed that their performance is highly compromised when exposed to high temperatures, as expected during fire incidents, which critically affects FRP–concrete bond behavior, hence affecting the overall efficiency of the strengthening system. This paper critically presents the available literature concerning the degradation of bond strength between FRP systems with concrete substrates due to increased temperatures. Both analytical and numerical bond–slip models developed for the prediction of bond strength degradation under such conditions are reviewed. A generally confirmed fact is that exposure to high temperatures, especially those reaching glass transition temperature (Tg) for epoxy adhesives, leads to bond degradation. Therefore, cement mortar-bonded CFRP textiles display better performance in fire endurance. This present paper also utilizes machine learning algorithms for the prediction of bond strength under elevated temperatures based on an experimental database of 37 beams. The nonlinear relationships and variable interactions in the developed model provide a reliable method for the estimation of bond strength with reduced extensive experimental testing, where the critical role of temperature in bond behavior is identified. This paper emphasizes the use of advanced predictive models to ensure the durability and safety of FRP-strengthened concrete structures in thermally challenging environments.

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R2 score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering.

Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete

Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.

Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length

Three-dimensional concrete printing (3DCP) faces challenges in determining and ensuring adequate bond strength between reinforcement and printed concrete. Traditional methods for predicting bond performance are merely deterministic without considering potential uncertainty, which would lead to risks for structural safety. To address this issue, this paper develops a trustworthy machine learning based prediction model for bond strength in reinforced printed concrete (RPC) structures using Natural Gradient Boosting algorithm. This developed model provides both scalar bond strength predictions and corresponding standard deviations, and in the test, it achieved a 94.5% safety rate and outperformed empirical formulas and deterministic approaches. Instructive guidance can be offered for structural engineers and designers in determining reinforcement embedment lengths for 3D-printed concrete during constructions. This probabilistic prediction approach can further enhance the safety and efficiency of digitally fabricated concrete structures, potentially extending its application to other critical parameters in printed concrete.

Analysis of bond strength of CFRP cables with concrete using random forest model

The use of carbon fiber-reinforced polymer (CFRP) as an alternative to steel tendons in prestressed concrete is increasingly favored due to its non-corrosive properties. A critical aspect of this substitution is the bond strength required to effectively transfer pre-stressing force. This study investigates the bond strengths of CFRP rods, CFRP strands, and steel strands following ISO 10406 standards. The research comprehensively explores the influence of cable type, cable surface roughness, and concrete strength on bond-slip behavior. Experimental results were augmented using a data augmentation method to enrich the dataset, which facilitated the development of a robust random forest (RF)-based prediction model for bond strength. The RF model achieved strong performance metrics, including a mean absolute percentage error (MAE) of 17.9 % and an R-value of 0.97, underscoring its efficacy in predicting bond strength. Notably, the model highlighted the significant impact of cable surface roughness on bond strength relative to other parameters studied based on the feature importance analysis. This study contributes to advancing the understanding of CFRP's applicability as a steel tendon replacement in prestressed concrete structures. The findings emphasize the importance of surface characteristics in optimizing bond strength, providing valuable insights for structural engineers and material scientists.

Bond strength prediction of externally bonded reinforcement on groove method (EBROG) using MARS-POA

The Externally Bonded Reinforcement on Grooves (EBROG) method represents an advancement in externally bonded reinforcement (EBR) techniques, specifically addressing the challenge of premature debonding often encountered in conventional Fiber Reinforced Polymer (FRP) applications directly bonded to concrete. This article introduces a novel and straightforward mathematical equation for predicting bond strength in the EBROG method using soft computing techniques for the first time. The study delves into the combined potential of the Multivariate Adaptive Regression Spline (MARS) model and the Pelican Optimization Algorithm (POA) for bond strength prediction in this method. The input parameters include FRP width, FRP thickness, elasticity modulus of FRP, concrete strength, groove length, groove width, and groove depth, while the output is EBROG bond strength. The study demonstrates exceptional accuracy with R2 values of 0.9629 for training and 0.9623 for testing, highlighting the model’s precision. The proposed bond strength prediction equation for the EBROG method undergoes validation against existing models, encompassing thirteen equations for EBR and a recent one specific to EBROG. Statistical metrics confirm the accuracy and reliability of the proposed equation. Notably, FRP stiffness emerges as the parameter with the highest relative importance, while groove width exhibits the lowest impact on bond strength.

Data-driven predicting of bond strength in corroded BFRP concrete structures

The mechanical properties of basalt fiber-reinforced polymer (BFRP) concrete structures in corrosive environments are largely dependent on the bonding performance between the BFRP reinforcement and concrete. An accurate and convenient method for calculating the bonding strength is crucial for engineering design. However, experimental methods and existing empirical models are insufficient to encompass the intricate interdependencies among the bonding factors. This study proposed a machine learning (ML)-based bonding strength prediction method, which utilizes random forests (RF) and adaptive boosting (AdaBoost) algorithms to predict the interfacial bond strength of BFRP reinforcement in corroded concrete. The model was trained on a dataset of 355 samples, effectively quantifying the relationships between the BFRP reinforcement parameters, concrete parameters, corrosion factors, and bonding strength. The performance of the two algorithms was evaluated using R2, RMSE, and MAE indicators, and the prediction results were explained using the SHAP method. The results show that the predicted results of the machine learning model are highly consistent with the experimental results, with the AdaBoost model achieving an R2 of 0.925 on the test set, demonstrating high predictive performance. Among the various factors, the corrosion factor has the most significant impact on bonding strength, followed by concrete compressive strength and BFRP bar yield strength. Ultimately, the model was juxtaposed against traditional empirical formulas, affirming its efficacy and dependability. The research presents a novel approach to predicting bond strength in corroded BFRP bar-concrete systems.

Bond strength prediction of FRP reinforced concrete using soft computing techniques

Bond strength prediction of FRP reinforced concrete using soft computing techniques

Machine learning enhanced modeling of steel‐concrete bond strength under elevated temperature exposure

AbstractThis study uses machine learning techniques to investigate the bond strength between steel and concrete under various elevated temperature scenarios. Five distinct machine learning algorithms, including Random Forest (RF), XGBoost, AdaBoost, Decision Tree, Linear Regression, and hyperparameteric optimisations, were used to predict changes in bond strength. The models underwent rigorous optimisation using GridSearchCV to achieve optimal performance. In this study, we evaluated several metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2) Score to compare and assess the models' prediction capabilities. After optimisation, results indicate that the RF model exhibited exceptional performance in estimating bond strength across different temperature conditions, demonstrating minimal errors and a high R2 Score. Visual comparisons of actual and predicted values further confirmed the efficacy of the RF model in capturing complex fluctuations in bond strength. The findings of this study underscore the potential of machine learning models, particularly the optimized RF method, in accurately predicting bond strength under varying thermal conditions, with promising implications for engineering and construction practices.

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

Analytical model for evaluating bond strength of steel rebar in cracked concrete considering confinement effect

This study delves into the intricate relationship between concrete cracking and bond strength, a critical factor in transferring forces between concrete and steel rebars. While previous research has often overlooked the impact of concrete cracking on bond strength and overestimated the beneficial effects of external confinement, this study aims to fill these gaps. A new failure criterion is introduced to predict bond strength of steel rebar in cracked concrete, considering the influence of concrete crack roughness and steel rebar surface features on force transfer at their interface. This criterion incorporates an interlock capacity coefficient that considers crack kinematics and the mentioned factors, enhancing the evaluation of effective compressive strength of cracked concrete in the limit analysis. Leveraging the Mohr-Coulomb failure criterion, an adaptable failure criterion accounts for crack kinematics, concrete damage, and confinement level to assess force transfer at the concrete/steel rebar interface. The developed criterion offers a practical calculation method to estimate bond strength in cracked reinforced concrete (RC) members with various confinement setups. Validation of this criterion and approach involved comparison against experimental results from 127 specimens, confirming its effectiveness and reliability in predicting bond strength in real-world scenarios.