Shear Strength Prediction Model Research Articles

Shear tests and meso-scale simulation of cold joint structures in rock-filled concrete: Considering the mutual contact effects between component materials

To realistically reflect the shear performance of the rock-filled concrete (RFC) between layers, this study relies on an RFC dam project in Guizhou Province. Test blocks, measuring 5 m × 4 m × 2 m with cold joints, were cast on site and subjected to shear tests. Additionally, a three-dimensional four-phase meso-model was developed, considering the mutual contact effects between component materials. The mechanical response and damage mechanism of the interlayer were analyzed using the cohesive zone model. The results confirm that the cohesive zone model effectively simulates interlayer cold joint structures. The damage modes of the RFC interlayer include cold joint debonding damage (Mode I) and plastic extrusion damage of self-compacting concrete (Mode II). As the exposed height of the rocks increases, the damage mode shifts from Mode I to Mode II, and higher normal stresses intensify Mode II damage. Meanwhile, the paths of crack extension depend on the distribution of shear-resistant rocks, with weak interfacial transition zones serving as “bridges’’ for crack propagation. The interlayer shear strength is positively correlated with the average exposed height of rocks and normal stresses, with the former having a more significant impact. Finally, the accuracy of the proposed shear strength prediction model for the RFC interlayer was validated by comparing it with results from other studies, providing useful references for meso-numerical modeling of RFC.

Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method

The peak shear strength of a rock joint is an important indicator in rock engineering, such as mining and sloping. Therefore, direct shear tests were conducted using an RDS-200 rock direct shear apparatus, and the related data such as normal stress, roughness, size, normal loading rate, basic friction angle, and JCS were collected. A peak shear strength prediction model for rock joints was established, by which a predicted rock joint peak shear strength can be obtained by inputting the influencing factors. Firstly, the study used the correlation analysis method to find out the correlation coefficient between the above factors and rock joint peak shear strength to provide a reference for factor selection of the peak shear strength prediction model. Then, the JRC-JCS model and four established GA-BP neural network models were studied to identify the most valuable rock joint peak shear strength prediction method. The GA-BP neural network models used a genetic algorithm to optimize the BP neural network with different input factors to predict rock joint peak shear strength, after dividing the selected data into 80% training set and 20% test set. The results show that the error of the JRC-JCS model is a little bigger, with a value of 11.2%, while the errors of the established GA-BP neural network models are smaller than 6%, which indicates that the four established GA-BP neural network models can well fit the relationship between the peak shear strength and selected input factors. Additionally, increasing the factor number of the input layer can effectively improve the prediction accuracy of the GA-BP neural network models, and the prediction accuracy of the GA-BP neural network models will be higher if factors that have higher correlation with the output results are used as input factors.

A Novel Model for Shear Strength Prediction of a Steel–UHPC Composite Structure Considering Interface Friction

A Novel Model for Shear Strength Prediction of a Steel–UHPC Composite Structure Considering Interface Friction

Development of shear strength prediction model for RC beams strengthened with FRP strips based on a novel ensemble learning model

The use of fiber-reinforced polymer (FRP) strips as an external strengthening method has gained widespread acceptance for enhancing the capacity of aging reinforced concrete (RC) beams. Despite extensive research and practical applications, there exist unresolved issues necessitating further investigation, including interface bonding, failure mechanisms, and the development of accurate predictive models for shear strength, considering diverse parameters. This paper systematically reviews shear strength formulas applied in the strengthening of RC beams using FRP strips. An extensive experimental dataset is compiled from diverse sources to support the analysis. Utilizing this dataset, a novel ensemble learning model is developed for predicting the shear strength contributed by FRP strips. Following the training of this model, the important score of each input parameter is also assessed. The findings presented herein contribute to a deeper understanding of the strengthening effectiveness of FRP strips on RC beams.

Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups

Advanced machine learning (ML) models are utilized for accurate shear strength prediction of reinforced concrete beams (RCB), but their lack of interpretability makes it unclear how models make specific predictions, reducing their reliability and applicability. Mainstream model-agnostic interpretation methods require numerous additional computing procedures, limiting the interpretation efficiency, and the model prediction process remains unknown. This study proposes an inherently interpretable shear strength prediction model for RCB with stirrups based on Explainable Boosting Machine (EBM) and Bayesian optimization. The EBM algorithm decomposes the predicted shear strength into individual shape functions, thus thoroughly revealing the prediction process. Besides, the ML tasks of selecting a model with optimal hyperparameters are automatically performed by Bayesian optimization to reduce computational cost. The developed model is validated on a database including 372 specimens. Compared with shear design codes, empirical models and prominent ML models, the EBM model obtains most accurate predictions for the test set with R2, MAE, and RMSE of 0.9293, 35.42 kN, and 50.99 kN, respectively. The accuracy of EBM is robust to varying input variables through trend analysis. Its interpretability fully discloses the contribution of individual features to the shear strength, and the model rationality is verified by comparing feature contribution with existing mechanisms. The proposed EBM model achieves inherently interpretable shear strength predictions while maintaining high accuracy, which promotes the model applicability in structural assessment.

ViT-Based Image Regression Model for Shear-Strength Prediction of Transparent Soil

The direct-shear test is the primary method used to test the shear strength of transparent soil, but this experiment is complex and easily influenced by experimental conditions. In order to simplify the process of obtaining the shear strength of transparent soil, an image regression model based on a vision transformer (ViT) is proposed in this paper; this is used to recognize the shear strength of the soil based on images of transparent-soil patches. This model uses a convolutional neural network (CNN) to decompose the transparent-soil images into multiple image patches containing high-order features, utilizes a ViT for feature extraction, and designs a regression network to facilitate the transfer of information between the abstract image features and shear strength. This model solves the problem of boundary blurring and difficult-to-identify features in speckle images. To demonstrate the effectiveness of the proposed model, different parameters related to transparent soil were obtained by controlling the particle size of fused quartz sand and the content of aerosol; in addition, the friction angle and cohesive force of the transparent soil under different proportions were measured using direct-shear tests, serving as two datasets. The results show that the proposed method achieves correlations of 0.93 and 0.94 in the two prediction tasks, thus outperforming existing deep learning models.

Seismic response and failure mechanism of ordinary concrete and UHTCC short columns reinforced with negative Poisson's ratio steel bars

The replacement of ordinary steel bars by negative Poisson's ratio (NPR) steel bars to improve the seismic performance of short columns is investigated in this study. NPR steel bars is a new type of reinforcement with negative Poisson's ratio effect and high strength and ductility. In this paper, six short column members were designed. Among them, P-RC1 and P-UHTCC1 short columns were used as controls, and the remaining four specimens were reinforced by NPR steel bars. The failure mode, cracking pattern, hysteresis characteristics, plastic deformation capacity, shear strength, stiffness degradation and energy dissipation capacity of the specimens were investigated through lateral cyclic loading tests. The experimental results showed that the shear capacity of short concrete columns reinforced with NPR steel bars increased by 20.91%−22.64% and the cumulative energy dissipation capacity increased by 45.7%−63.3%. The short UHTCC columns reinforced with NPR steel bars showed an increase in shear capacity of 23.08%−35.48% and an increase in cumulative energy consumption capacity of 17.3%−28.5%. And the RC columns shear strength prediction model based on GB50010–2010 specification is relatively reasonable to be used for the evaluation of shear strength of short columns reinforced with NPR steel bars.

Shear Strength Evaluation of Concrete Beams with FRP Transverse Rebar

Rebar corrosion in traditional reinforced concrete (RC) components may lead to a decrease in service life and carrying capacity. This condition is one of the reasons of the growing popularity of Fiber Reinforced Polymer (FRP) rebars as a corrosion-resistant alternative, particularly in RC infrastructure projects. Because the material properties and behavior of FRP rebar are very different from conventional steel rebar, the calculations used for reinforced concrete with conventional steel reinforcement should be updated for this material. The aim of this study is to propose a new shear strength prediction model for RC beams with transverse steel rebar in order to calculate the shear strength of RC beams with FRP transverse rebar according to TS-500, which is the Turkish Building Code. To achieve this goal, Finite Element Method (FEM) models were created for 27 RC beams with FRP transverse rebars and 9 RC beams without transverse rebars. Furthermore, for RC beams with FRP transverse rebars, a prediction model has been developed. Additionally, 13 prediction models obtained from regulations or scientific studies were compared to the proposed prediction model using a database of 105 tests obtained from previous experimental studies. It was observed that the proposed prediction model provides more consistent results with the test database from the literature compared to the models suggested by other regulations or studies. Therefore, by modifying the shear strength relations recommended in TS-500 for RC beams with transverse steel rebar, they can also be applied to RC beams with transverse FRP rebars.

BO-Stacking: A novel shear strength prediction model of RC beams with stirrups based on Bayesian Optimization and model stacking

Shear strength prediction for reinforced concrete (RC) beams is a complex non-linear problem impacted by many factors. Traditional Machine Learning (ML) models for shear strength prediction still need manual single-model selection and hyperparameter optimization, limiting the model deployment efficiency and prediction accuracy in practical engineering. This study proposes a novel shear strength prediction model based on Bayesian Optimization and multi-layer stacking (BO-Stacking). It trains and stacks several base ML models in a multi-layer structure to avoid manual model selection and boost shear strength prediction accuracy concurrently. Besides, Bayesian Optimization is developed to automatically find optimal hyperparameters of each base ML model, substantially reducing computational cost during training. With the developed techniques, the proposed model is able to automatically achieve high accuracy by simply specifying training time. Furthermore, SHapley Additive exPlanation is adopted to interpret the feature contributions in shear strength prediction. The proposed model is implemented on an experimental dataset consisting of 406 RC beams with stirrups and then compared with seven base ML models and three shear design codes. Comparative results show that BO-Stacking achieves the highest accuracy on the test set with R2, RMSE and MAE of 0.9551, 39.98 and 25.99 respectively. The reliability and robustness of BO-Stacking is further verified through the trend analysis and Monte Carlo simulation. The proposed BO-Stacking model automates the tedious tasks of implementing an ML model for shear strength prediction and achieves stable and accurate results, facilitating the efficiency and reliability of model deployment in practice.

Prediction Model of Residual Soil Shear Strength under Dry–Wet Cycles and Its Uncertainty

Granite residual soil is widely distributed in Southeast Fujian. Large-scale engineering construction leads to the exposure of residual soil slopes to the natural environment. Affected by seasonal climate factors, the soil of slopes experiences a dry–wet cycle for a long time. The repeated changes in water content seriously affect the shear strength of soil, and then affect the stability of the slope. In order to explore the influence of the dry–wet cycle on the shear strength of granite residual soil in Fujian, an indoor dry–wet cycle simulation test was carried out for shallow granite residual soil on a slope in Fuzhou, and the relationship between water content, dry–wet cycle times, and the shear strength index, including the cohesion and internal friction angle of the granite residual soil, was discussed. The results show that when the number of dry–wet cycles is constant, the cohesion and internal friction angle of the granite residual soil decrease with an increase in water content. The relationship between the cohesion, internal friction angle, and water content can be described using a power function. Meanwhile, the fitting parameters of the power function are also a function of the number of wet and dry cycles. The prediction formulas of the cohesion and internal friction angle considering the number of dry–wet cycles and water content are established, and then the prediction formula of shear strength is obtained. The ratio of the predicted value of shear strength to the test value shall be within ±15%. An error transfer analysis based on the point estimation method shows that the overall uncertainty of the predicted value of shear strength caused by the combined uncertainty of the predicted value of cohesion and the internal friction angle and the single-variable uncertainty of the predicted value of shear strength caused only by the uncertainty of the predicted value of either the cohesion or internal friction angle increases first and then decreases with an increase in the number of dry–wet cycles. All increase with an increasing water content. The maximum standard deviation of the proposed shear strength prediction model of granite residual soil is less than 9%.

Design oriented shear strength prediction model of UHPFRC beams

The application of Ultra-high performance fiber reinforced concrete (UHPFRC) to beams can effectively improve their strength and ductility properties. At present, there are more shear test studies on UHPFRC beams, but few theoretical studies on their strength calculation method. The fibers in UHPFRC can inhance the strength of the beams, and how to quantify the fibers’ contribution to the total strength is a research difficulty. Firstly, the shear strength database including 197 UHPFRC beam specimens is created. The key characteristics determining the shear strength, namely fiber volume fraction, stirrup configuration, and shear-span ratio, are determined by evaluating the test results. Then, considering the effects of the factors above, a truss-arch model is developed. The arch shear strength is determined by utilizing the shear deformation coordination of the arch compression structs and truss compression structs. Superimposing the shear strengths of the arch and truss models yields the shear strength of UHPFRC beams. Finally, the accuracy and reliability of the proposed method are assessed using the test database and compared to existing shear strength calculations. The study findings demonstrate that the mean value of the calculated to observed shear strength ratios produced by the suggested technique is 1.10, with a coefficient of variation of 0.28, indicating that the proposed method has superior calculation accuracy and reliability than existing methods.

Investigation on shear mechanical behavior of the interface between sulphoaluminate cement (SAC)-ECC and existing concrete

Sulphoaluminate cement-engineered cementitious composites (SAC-ECC) have the characteristics of fast hardening and early strength. Hence, this can be applied to repair and strengthen building structures. In the present study, the interface shear strength between SAC-ECC and existing concrete was tested. The results revealed that both the interfacial roughness of existing concrete and curing age of SAC-ECC have a significant influence on the interface shear strength between SAC-ECC and existing concrete. All specimens exhibited a tendency for the interface shear strength to increase with the increase in interfacial roughness of existing concrete, and increase in curing age of SAC-ECC. A shear strength prediction model that considered the interfacial roughness of existing concrete and curing age of SAC-ECC was proposed. The accuracy of the proposed model for predicting interface shear strength was verified by comparing it with the experimental results. Under shear loading, the interface bonding force between SAC-ECC and existing concrete consisted mainly of the chemical gluing force and mechanical interlocking force. The van der Waals force was negligible.

Three-Dimensional Stability of Unsaturated Soil Slopes Reinforced by Frame Beam Anchor Plates

The frame beam anchor plate (FBAP) is a new supporting structure to reinforce backfill. The current studies of slope stability concentrate on homogeneous isotropic dry or saturated slopes according to two-dimensional assumptions. In engineering practice, however, the soils are heterogeneous and anisotropic and exhibit evident unsaturated characteristics, and the failure surface shows obvious three-dimensional (3D) characteristics universally. A new method for evaluating the 3D stability of unsaturated soil slopes reinforced with FBAPs is established in this paper. The upper-bound analytical solution of slope safety factor (Fs) is derived based on the energy balance equation incorporated with the 3D spiral failure mechanism and the gravity increase method (GIM). Comparisons verified the effectiveness of the methodology and the optimization program. The influence of unsaturated characteristics, support structure design parameters, and seismic force on the 3D slope stability is evaluated through parameter analysis. The results show that the shear strength prediction model is crucial in slope stability assessments. The stability of slopes can be significantly improved by about 29%–52% by using FBAPs. Seismic action has a significant negative impact on the slope stability. When kh increases from 0 to 0.3, Fs generally decreases by about 40%–55%.

Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm

Currently, concrete structures have increasingly higher requirements for the shear capacity of beams, and ultrahigh-performance concrete (UHPC) beams are increasingly widely used. To facilitate the design of UHPC beams, this paper constructs a UHPC beam shear strength prediction model. First, static shear tests were conducted on 6 UHPC beam specimens with a length of 2 m and a cross-sectional size of 200 mm × 300 mm to explore the effects of the UHPC strength, shear span ratio, hoop ratio, and steel fiber content on the shear resistance and failure morphology of the UHPC beams. Based on the results of this study and a static load experiment of 102 UHPC beams in the literature, the construction includes the shear span ratio (λ), beam section width (b), beam section height (h), hoop ratio (ρSV), UHPC compressive strength (fc), steel fiber volume fraction (Vf), and the UHPC beam shear capacity (Vex) 7 parameter database. Based on the construction of the database, 1200 BPNN models were trained through trial and error. The models were evaluated using the correlation coefficient R, root mean square error RMSE, and a20-index indicators, and the optimal BPNN model (6-15-8-1) was determined based on the ranking of RMSE. After the optimal BPNN is optimized by a genetic algorithm, the prediction performance of the model is improved. The correlation coefficient between the predicted value and the experimental value is R2 = 0.98667, and RMSE = 7.38. This model can reliably predict the shear strength of UHPC beams and provide designers with a reference for the design of UHPC beams. Finally, after sensitivity analysis, the influence of each input parameter on the UHPC shear capacity is determined.

Simplified static shear strength prediction model for adhesively bonded joints assembled with brittle adhesives

The use of structural adhesives has gained a considerable importance in the last decade, both in scientific research and in industrial production. Thanks to the numerous advantages offered by adhesive bonding, there are now numerous fields of application (e.g., automotive, aerospace, civil engineering). Therefore, the study of the stress distribution in the adhesive region and the prediction of the ultimate strength of the joint are currently topics of great interest in the literature. However, numerous methods used for determining strength require nonlinear numerical procedures in conjunction with extensive experimental campaigns to define a specific failure criterion. In this paper, a simplified method is proposed for determining the failure load of single-lap and double-lap adhesive joints assembled with quasi-brittle adhesives. Static-linear analysis of the stress state of the joint is illustrated, and the shear stress distribution is determined in closed form. The yield point of the adhesive layer is considered as the failure criterion, which can be estimated from tensile tests on adhesive specimens (i.e., dogbones). Therefore, the failure of the joint is considered to be reached as soon as the tangential stresses reach a value corresponding to the yield strength of the adhesive. In this way, a closed-form solution to the problem is proposed, which can be useful for the design of the joint.

Shear Strength Prediction Model for RC Exterior Joints Using Gene Expression Programming.

Predictive models were developed to effectively estimate the RC exterior joint’s shear strength using gene expression programming (GEP). Two separate models are proposed for the exterior joints: the first with shear reinforcement and the second without shear reinforcement. Experimental results of the relevant input parameters using 253 tests were extracted from the literature to carry out a knowledge analysis of GEP. The database was further divided into two portions: 152 exterior joint experiments with joint transverse reinforcements and 101 unreinforced joint specimens. Moreover, the effects of different material and geometric factors (usually ignored in the available models) were incorporated into the proposed models. These factors are beam and column geometries, concrete and steel material properties, longitudinal and shear reinforcements, and column axial loads. Statistical analysis and comparisons with previously proposed analytical and empirical models indicate a high degree of accuracy of the proposed models, rendering them ideal for practical application.

Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network

• The oblique shear test and 90° vertical shear test device were designed. • The interlayer shear tests of asphalt mixtures with different composite structures were carried out. • The BP neural network prediction model for the shear strength of asphalt mixture specimens were established. • The prediction model were verified by experiments. Given the complexity of the factors that affect the interlayer shear strength of asphalt pavement in typical steeply-sloped sections of areas with seasonally frozen soil, to predict and evaluate interlayer shear strength more accurately and rapidly, this study obtains experimental data by designing indoor direct and oblique shear tests, and establishes a three-layer improved back propagation (BP) neural network model to predict the interlayer shear strength of pavement with a structure of 6–20-1. The model uses 6 influencing factors of specimen combination types-tack coat type, a tack coat dosage, shear angle, temperature, and loading rate-as the input layer. The neural network trained, verified, and tested 230 sets of oblique shear test data, and completed the neural network’s universality test. The research results show that the predictive value of the shear strength under different viscous layer oil dosages and temperatures is very consistent with the results of universal testing experiments. Different types of specimens’ interlayer shear strength increases first and then decrease as the amount of tack coat increases, and compared with base asphalt and emulsified asphalt, SBS-modified asphalt has the best interlayer shear resistance when used as the tack coat, and the optimal dosage is 1.2 kg/m 2 . Temperature is under a significant negative correlation with the specimen’s interlayer shear strength. As the temperature rises from 20 to 60℃, the interlayer shear strength of different specimen types decreased at a faster rate, and the shear strength at 58℃ was only 15 %-30 % of that at 20℃. The constructed BP neural network prediction model has good convergence and superior performance. The model prediction error does not exceed ± 0.5, and the prediction accuracy is high (R 2 = 0.99). At the same time, the shear specimen’s shape does not affect the use of the interlayer shear strength prediction model, which can be utilized to predict the interlayer shear strength of the asphalt mixture specimens.

Effect of Freeze–Thaw Cycles on Shear Strength of Tailings and Prediction by Grey Model

Tailings dams in the seasonal frozen regions experience freeze–thaw cycles with the change in natural geography and climatic conditions, which may have a strong influence on the mechanical properties of the tailings. In this paper, the effects of freeze–thaw cycles on the mechanical properties and pore structure of tailings were investigated. Triaxial tests were carried out on tailings with different moisture contents (5%, 10%, 15%, 20%) under different confining pressures (50 kPa, 100 kPa, 200 kPa, 300 kPa) after different freeze–thaw cycles (10, 20, 30, 40, 50). The pore structures of tailings were quantitatively analyzed as well. Furthermore, grey system theory was applied to develop a shear strength prediction model for tailings in cold regions. The results showed that the optimal moisture content of tailings fell 10%–15%. The shear strength of the tailings increased under higher confining pressures, while it decreased after more freeze–thaw cycles. Irrecoverable large pore deformation between particles within the tailings was found after 40 freeze–thaw cycles. After 50 freeze–thaw cycles, the proportion of pores larger than 100 μm increased from 22.76% to 48.45%. Predictions based on the Grey Model were found to be consistent with the test results and the shear strength test law. The residual error and class ratio dispersion of the model were less than 0.2, indicating that the Grey Model has high prediction accuracy and thus can be used for the prediction of the shear strength of tailings.

Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting Gene Expression Programming.

In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.

Simplified Strut-and-Tie Model for Shear Strength Prediction of Reinforced Concrete Low-Rise Walls

First Name is required invalid characters Last Name is required invalid characters Email Address is required Invalid Email Address Invalid Email Address