Search Results (67)

Reinforced concrete (RC) deep beams constructed with low-strength concrete are susceptible to sudden splitting failures in the strut region due to shear–compression stresses. To mitigate this vulnerability, various strengthening techniques, including steel plates, fiber-reinforced polymer sheets, and cementitious composites, have been explored to confine the strut area. This study investigates the structural performance of RC deep beams with low-strength concrete, strengthened externally using an Engineered Cementitious Composite (ECC) layer. To ensure effective confinement and uniform shear distribution, shear reinforcement was provided at equal intervals with configurations of zero, one, and two vertical shear reinforcements. Four-point bending tests revealed that the ECC layer significantly enhanced the shear capacity, increasing load-carrying capacity by 51.6%, 54.7%, and 46.7% for beams with zero, one, and two shear reinforcements, respectively. Failure analysis through non-linear finite element modeling corroborated experimental observations, confirming shear–compression failure characterized by damage in the concrete struts. The strut-and-tie method, modified to incorporate the tensile strength of ECC and shear reinforcement actual stress values taken from the FE analysis, was used to predict the shear capacity. The predicted values were within 10% of the experimental results, underscoring the reliability of the analytical approach. Overall, this study demonstrates the effectiveness of ECC in improving shear performance and mitigating strut failure in RC deep beams made with low-strength concrete. Full article

Fiber-reinforced polymer (FRP) bars have great potential to replace steel bars in the design of reinforced concrete (RC) beams since they have numerous advantages such as high tensile strength and good corrosion resistance. Therefore, many studies including experiments and numerical simulations have focused on the behavior of FRP RC beams. In this paper, a comprehensive overview of previous studies is conducted to provide a thorough understanding about the behavior, the design, and the limitations of FRP RC beams. Particularly, experimental studies on FRP RC beams are collected and reviewed. In addition, the numerical analysis of FRP beams including the finite element (FE) analysis, the discrete element (DE) analysis, and artificial intelligence/machine learning (AI/ML) is summarized. Moreover, the international standards for the design of FRP RC beams are presented and evaluated. Through the review of previous studies, 93 tested specimens are collected. They can be a great source of reference for other studies. In addition, it has been found that the studies on the continuous beams and deep beams reinforced with FRP bars are still limited. In addition, more studies using DE analysis and AI/ML to analyze the response of FRP RC beams under loading conditions should be conducted. Full article

Reinforced concrete deep beams (RCDBs) provide significant strength and serviceability for building structures. However, a simple, general, and universally accepted procedure for predicting their shear strength (SS) has yet to be established. This study proposes a novel data-driven approach to predicting the SS of RCDBs using an enhanced CatBoost (CB) model. For this purpose, a newly comprehensive database of RCDBs with shear failure, including 950 experimental specimens, was established and adopted. The model was developed through a customized procedure including feature selection, data preprocessing, hyperparameter tuning, and model evaluation. The CB model was further evaluated against three data-driven models (e.g., Random Forest, Extra Trees, and AdaBoost) as well as three prominent mechanics-driven models (e.g., ACI 318, CSA A23.3, and EU2). Finally, the SHAP algorithm was employed for interpretation to increase the model’s reliability. The results revealed that the CB model yielded a superior accuracy and outperformed all other models. In addition, the interpretation results showed similar trends between the CB model and mechanics-driven models. The geometric dimensions and concrete properties are the most influential input features on the SS, followed by reinforcement properties. In which the SS can be significantly improved by increasing beam width and concert strength, and by reducing shear span-to-depth ratio. Thus, the proposed interpretable data-driven model has a high potential to be an alternative approach for design practice in structural engineering. Full article

Access to significant amounts of data is typically required to develop structural health monitoring (SHM) systems. In this study, a novel SHM approach was evaluated, with all training data collected solely from a validated finite element analysis (FEA) of a reinforced concrete (RC) beam and the structural health based on the tension side of a rebar under flexural loading. The developed SHM system was verified by four-point bending experiments on three RC beams cast in the dimensions of 4000 mm × 200 mm × 400 mm. Distributed optical fibre sensors (DOFS) were mounted on the concrete surface and on the bottom rebar to maximise sample points and investigate the reliability of the strain data. The FEA model was validated using a single beam and subsequently used to generate labelled SHM strain data by altering the dilation angle and rebar sizes. The generated strain data were then used to train an artificial neural network (ANN) classifier using deep learning (DL). Training and validation accuracy greater than 98.75% were recorded, and the model was trained to predict the tension state up to 90% of the steel yield limit. The developed model predicts the health condition with the input of strain data acquired from the concrete surface of reinforced concrete beams under various loading regimes. The model predictions were accurate for the experimental DOFS data acquired from the tested beams. Full article

Steel-reinforced concrete (SRC) deep beams have been widely used in engineering applications such as high-rise buildings and long-span bridges, with their structural behavior and mechanical properties attracting significant research attention. To investigate the shear cracking behavior of SRC deep beams, seven specimens with a scale of 0.4 times were designed for static loading tests, and the influence of the shear-span-to-depth ratio λ, the width ratio of the steel flange, and the height ratio of the steel web on the width and spacing of the diagonal crack was considered. The cracking behavior of the diagonal cracks in the shear span area were recorded by the digital image correlation (DIC) technique. The results show the following: (1) the use of the DIC technology revealed the entire process of crack occurrence, development, and evolution and obtained the distribution characteristics of crack development; (2) the steel flange width has a slight effect on the spacing and width of the diagonal cracks. The diagonal crack width increased with the improvement of the height of the steel web, but the influence of the steel web on the spacing of diagonal cracks was not significant. When the height ratio increased from 0.3 to 0.45 and 0.6, the maximum oblique crack width increased by 13% and 14.5%. Based on the above experimental results and relevant analysis conclusions, an improved method was proposed to calculate the diagonal crack width of composite deep beams by further considering the influence of the crack angle. Finally, the experimental results verified its high accuracy in a qualitative analysis. The calculation method proposed in this article can be used to predict and estimate the width of diagonal cracks in SRC deep beams. Full article

Aluminium alloy (AA) bars have emerged in structural engineering applications mainly to reduce deterioration caused by corrosion. However, research on AA-reinforced concrete (RC) beams has been limited, despite RC beams reinforced with AA bars providing a study area with great potential. Therefore, this study mainly aims to investigate the behaviour of AA RC deep beams. The investigated parameters include concrete strength, tension reinforcement ratio, beam size, a/d ratio, and transverse reinforcement ratio, most of which have not yet been thoroughly studied. A finite element (FE) model was developed to obtain accurate predictions. The developed FE model predicted the actual load-bearing capacity with a mean value of 1.00. The findings indicated a clear trend in which shear force capacity increased from 124.1 to 181.4 kN with increasing concrete compressive strength from 20 to 40 MPa. A strong relationship between the reinforcement ratio and failure mode was obtained. The shear strength decreased from 2.95 to 2.1 MPa as the effective depth increased from 175 to 350 mm. An increase in transverse reinforcement ratio instigated an enhancement in shear force capacity. Finally, the applicability of the design models in the current literature was evaluated. The design formulations gave accurate predictions with an error of 3%. Full article

This study investigates the issues of non-unique model configurations and insufficient guidance for reinforcement design encountered when applying the strut-and-tie model (STM) method to reinforced concrete deep beams with openings. Using concrete deep beam specimens with three openings as a case study, the topology optimization method was employed to establish the initial STM, which was subsequently refined through crack propagation simulation technology to develop the final optimized STM for guiding reinforcement design. Experimental investigations and comparative analyses with existing literature demonstrate that the proposed approach offers significant advantages in controlling initial concrete cracking, improving structural load-bearing capacity, and reducing steel reinforcement consumption for such perforated deep beams designed with this optimized STM methodology. Full article

Using artificial neural networks (ANN), numerous models were developed for predicting the ultimate shear strength of reinforced concrete deep beams. Many experimental result databases from earlier research were carefully gathered for this study. Two hundred fifty-three findings from experiments were included in this database. The ultimate shear strength was the output parameter, while ten factors were determined as input parameters for the ANN model based on the completed literature research. The required model was constructed using a back propagation neural network. The model of the neural networks was determined using the trial-and-error method. It was discovered that, inside the range of the input boundaries considered, the ANN model could accurately estimate the ultimate shear strength of deep beams. The measured shear strength and the shear strength predicted by the ANN model have a high correlation coefficient of 0.97, indicating a strong relationship between the predicted and actual values. The results show that, given the range of input parameters, ANN offers an excellent agreement of interest as a practical technique for estimating the ultimate shear strength. A parametric investigation was performed using the trained neural network model to assess how the input parameters affected the shear strength capacity of deep beams. Full article

The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. A database with diverse material properties associated with the shear strength of a steel fiber reinforced concrete beam was established from numerous reliable published research articles and was utilized for the development and evaluation of the model. The obtained results from the hybrid support vector regression model were then validated through the results of the artificial neural network and convolutional neural network models combined with the particle swarm optimization algorithm. In conclusion, the adopted hybrid support vector regression approach was proven to be a successful engineering technique that can be used in structural and construction engineering problems. Full article

Ultra-high-performance concrete (UHPC) has attracted considerable attention from both the construction industry and researchers due to its outstanding durability and exceptional mechanical properties, particularly its high compressive strength. Several factors influence the shear capacity of UHPC deep beams, including compressive strength, the shear span-to-depth ratio (λ), fiber content (FC), vertical web reinforcement (ρ_sv), horizontal web reinforcement (ρ_sh), and longitudinal web reinforcement (ρ_s). Considering these factors, this research proposes a novel hybrid algorithm that combines an adaptive neuro-fuzzy inference system (ANFIS) with an artificial neural network (ANN) to predict the shear capacity of UHPC deep beams. To achieve this, ANN and ANFIS algorithms were initially employed individually to predict the shear capacity of UHPC deep beams using available experimental data for training. Subsequently, a novel hybrid algorithm, integrating an ANN and ANFIS, was developed to enhance prediction accuracy by utilizing numerical data as input for training. To evaluate the accuracy of the algorithms, the performance metrics R² and RMSE were selected. The research findings indicate that the accuracy of the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was observed as R² = 0.95, R² = 0.99, and R² = 0.90, respectively. This suggests that despite not using experimental data as input for training, the ANN-ANFIS algorithm accurately predicted the shear capacity of UHPC deep beams, achieving an accuracy of up to 90.90% and 94.74% relative to the ANFIS and ANN algorithms trained on experimental results. Finally, the shear capacity of UHPC deep beams predicted using the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was compared with the values calculated based on ACI 318-19. Subsequently, a novel reliability factor was proposed, enabling the prediction of the shear capacity of UHPC deep beams reinforced with fibers with a 0.66 safety margin compared to the experimental results. This indicates that the proposed model can be effectively employed in real-world design applications. Full article

Ultra-high-performance concrete (UHPC) is considered a highly applicable composite material due to its exceptional mechanical properties, such as high compressive strength and ductility. UHPC deep beams are structural elements suitable for short spans, transfer girders, pile caps, offshore platforms, and bridge applications where they are designed to carry heavy loads. Several key factors significantly influence the shear behavior of UHPC deep beams, including the compressive strength of UHPC, the vertical web reinforcement (ρ_sv), horizontal web reinforcement (ρ_sh), and longitudinal reinforcement (ρ_s), as well as the shear span-to-depth ratio (λ), fiber type, fiber content (FC), and geometrical dimensions. In this paper, a comprehensive literature review was conducted to evaluate factors influencing the shear behavior of UHPC deep beams, with the aim of identifying research gaps and enhancing understanding of these influences. The findings from the literature were systematically classified and analyzed to clarify the impact and trends associated with each factor. The analyzed data highlight the effect of each factor on the shear behavior of UHPC deep beams, along with the overall trends. The findings indicate that an increase in compressive strength, FC, ρ_sv, ρ_s, and ρ_sh can enhance the shear capacity of UHPC-DBs by up to 63.36%, 63.24%, 38.14%, 19.02%, and 38.14%, respectively. Additionally, a reduction of 61.29% in λ resulted in a maximum increase of 49.29% in the shear capacity of UHPC-DBs. Full article

This study evaluates the performance of lightweight aggregate deep beams strengthened with low-cost glass fiber-reinforced polymer composite (Lo-G) wraps as an alternative to expensive synthetic fiber-reinforced polymers (FRPs). The investigation includes side-bonded and fully wrapped configurations of Lo-G wraps, alongside carbon FRP (CFRP) strips for comparison. The experimental results show that epoxy-based anchors provided significantly better resistance against de-bonding than mechanical anchors, improving beam performance. Strengthening with Lo-G wraps resulted in a peak capacity increase of 17.0% to 46.9% for side-bonded beams in Group 2, 10.5% to 41.4% for fully wrapped beams in the strip configuration in Group 3, and 15.4% to 42.7% for CFRP strips in Group 4. The ultimate deflection and dissipated energy were also improved, with dissipated energy increases of up to 264.6%, 322.3%, and 222.7% for side-bonded and fully wrapped Lo-G wraps and CFRP strips, respectively. The side-bonded configuration with two or three Lo-G wraps, supplemented by epoxy wraps, outperformed fully wrapped 250 mm strips in peak capacity, with peak capacity improvements of up to 46.9%. However, beams with mechanical anchors showed poor performance due to premature debonding. They rely on friction and expansion within the concrete to resist pull-out forces. If the surrounding concrete is not strong enough or if the anchor is not properly installed, it can lead to failure. Additionally, reducing strip spacing negatively impacted performance. Lo-G wraps showed an 8.5% higher peak capacity and 32.8% greater dissipated energy compared to CFRP strips. Despite these improvements, while Lo-G wraps are a cost-effective alternative, their long-term performance remains to be investigated. None of the existing models accurately predicted the shear strength contribution of Lo-G wraps, as the lower elastic modulus and tensile strength led to high deviations in prediction-to-experimental ratios, underscoring the need for new models to assess shear strength. Full article

The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%. Full article

In this study, the effectiveness was investigated of shear strengthening techniques in reinforced concrete (RC) deep beams incorporating stainless steel plates (SSPs). Four RC deep beams were tested under incremental static loading until failure to examine the proposed strengthening techniques. The key parameters considered in this study included the arrangement of the externally bonded SSPs. The experimental findings demonstrated that strengthening using SSPs led to substantial improvements in their performance compared to the unstrengthened control beam. The use of SSPs increased the ultimate shear capacity by 129 to 175% over the control specimen. Finite element models (FEMs) were developed to simulate the responses of the tested beams strengthened using SSPs. Parametric studies were then conducted using the validated FEM to investigate to identify the effects of the area of SSPs on the shear capacity of the beams. The parametric studies concluded that increasing the plate thickness resulted in the enhanced shear capacity of the deep beam specimens up to a critical point upon which the increases in the thickness have insignificant effects on the shear strength. The accuracy of the design equations given by European and American codes in predicting the shear strength of the deep beams is examined. Full article

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials’ structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals. Full article