Shear Strength Model Research Articles

Study on influencing factors and prediction model of strength and compression index of sandy silt on bank under freeze–thaw cycles

The Inner Mongolia section of the Yellow River is a seasonal frozen soil area, where the freeze–thaw effect can alter soil strength and compressibility, affecting bank stability. This study takes the banks sandy silt of the Inner Mongolia section of the Yellow River as the research object. It systematically investigates the relationship between shear strength parameters and compression index of sandy silt and the initial dry density, water content, and freeze–thaw cycles of the soil. It analyzes the order and significance of influencing factors, establishes prediction models of shear strength and compression index, and evaluates the effects of freeze–thaw cycles on soil cohesion and shear strength. The results show that the shear strength index of sandy silt is proportional to changes in initial dry density and inversely proportional to changes in water content. After 10 freeze–thaw cycles, the cohesion of the soil decreases by 22.53 to 58.85%, and the shear strength decreases by 22.67 to 58.91%. The internal friction angle is less affected by freeze–thaw and tends to be stable overall. The smaller the initial dry density and the greater the water content, the greater the compression index and compressibility of the soil, but freeze–thaw has little effect on compression index. The factors affecting sandy silt shear strength and compression index are ranked as dry density > moisture content > freeze–thaw cycles. The stepwise regression model of soil shear strength and compression index based on initial dry density, water content, and freeze–thaw cycles is effective, providing technical guidance for engineering practice.

In-plane shear strength models of masonry walls strengthened with steel-bar truss units

In-plane shear strength models of masonry walls strengthened with steel-bar truss units

Shear capacity model of RC beams based on MCMC approach and reliability analysis

The research question revolves around accurately predicting the shear capacity of RC beams and enhancing the reliability of such prediction models. This study aims to refine the accuracy of the RC beam shear capacity model. To augment the safety margin, the enhanced model underwent further calibration through a reliability analysis. The shear strength models, rooted in Bayesian theory’s Markov Chain Monte Carlo (MCMC) method and the Modified Compressive Field Theory (MCFT), were introduced. A research database was curated from test data of 782 RC beams sets under shear stress. From this database, six mechanism models were juxtaposed with the MCMC models for comparison. To meet the desired reliability indexes ([Formula: see text] = 3.2, 3.7, 4.0) in these models, resistance factors ([Formula: see text]) were incorporated. Both the Monte Carlo and First Order Second Moment (FOSM) methods were employed for the reliability analysis, considering inherent model errors and the probability distribution of primary variables. The effect of different elements on the target reliability index was assessed using a tornado diagram. Findings showed that the refined shear model displayed minimal variance when benchmarked against models from existing literature and national codes, yielding a Vtest/ Vcal ratio close to 1.0. The shear components aligned with established mechanical mechanism. Depending on various operational conditions and load combinations, resistance factors fluctuated from 0.377 to 0.516 in the RC beams without stirrup, while resistance factors fluctuated from 0.479 to 0.621 in the RC beams with stirrup. Key determinants, such as the inherent uncertainty in computing model, the live loads, and the cylinder compression strength, significantly influenced the reliability index.

Study of the Shear Strength Model of Unsaturated Soil in the Benggang Area of Southern China

Benggangs are a unique type of soil erosion commonly found in southern China, with the gully wall being the most dynamic component of the Benggang system and is crucial for assessing its overall progression. The unsaturated shear strength of soil in Benggang areas is a key factor influencing the stability of the gully wall. However, quantitative analyses of the unsaturated shear strength in the gully walls of Benggangs remain limited. In this study, the soil–water characteristic curves (SWCC) and shear strengths of undisturbed soil samples from four different soil layers in the gully wall of Benggang were measured using a pressure membrane meter and a quadruple direct shear apparatus. The results revealed that the water holding capacity of the soil decreased gradually with increasing matrix suction, and the order of the water holding capacity was the sandy soil layer > transition layer > laterite layer > clastic layer. With an increasing soil water content (SWC), the shear strength, cohesion (c), and internal friction angle (φ) of the four soil layers decreased significantly, and the φ showed a power function decreasing curve (p < 0.05), whereas c in the laterite layer and transition layer exhibited a power function decreasing curve (p < 0.01). The c of the sandy soil layer and clastic layer decreased linearly and logarithmically (p < 0.01) with increasing SWC, respectively. The unsaturated shear strength model for the four soil layers was developed based on the Vanapalli model. The root mean square error (RMSE) of the simulated and measured values was less than 29.349, while the Nash–Sutcliffe efficiency (NSE) and R2 values were greater than 0.638 and 0.788, respectively. The model can be used to analyze and predict the unsaturated shear strength in different layers of Benggang gully walls, providing a theoretical foundation for studying the erosion mechanisms of Benggangs.

Assessment of Shear Strength Models for Squat Rectangular Reinforced Concrete Shear Walls

The shear strength is a critical parameter in the design of Reinforced Concrete (RC) shear walls subjected to lateral loads. Numerous design codes and published studies have proposed formulas for calculating the shear strength of squat RC walls. However, there is a discrepancy between the calculated and experimental results. This study aims to evaluate various models for the calculation of the shear strength of rectangular squat RC walls using 312 databases collected from the literature. The shear strength of the RC walls was calculated using eight code- and empirical-based models, while the input parameters were obtained from the experimental database. The results were evaluated utilizing two statistical indicators: coefficient of determination and root-mean-squared error. The analysis of the results revealed that the model of C. K. Gulec and A. S. Whittaker is the optimal model, followed by the models of S. L. Wood and Eurocode-8 (EC8).

Leeb hardness test as a tool for joint wall compressive strength (JCS) evaluation

The Barton-Bandis model for the nonlinear shear strength of rock joints is the most commonly used strength criterion in rock engineering practice. There have been advancements in determination of Joint Roughness Coefficient (JRC), such as the use of laser scanning; however, the equally important Joint Wall Compressive Strength (JCS) parameter has not been significantly advanced. The JRC and JCS are effectively linked, to some extent. A sensitive rebound hardness index test, the Leeb Hardness (LH) test, was investigated to provide a quantifiable and repeatable method of JCS determination that offers increased accuracy relative to current methods. The LH test value (LD) correlation to Unconfined Compressive Strength (σc) is proposed for JCS determination. In addition, this study investigates the Influence Zone of the LH test on surfaces with graded hardness profiles (e.g., weathered surfaces). This was done using a series of artificial composite plaster-rock specimens of known hardness to provide insight into the influence effects on the surface LD reading due to underlying material of contrasting hardness. In addition, a collection of natural rock specimens with variable joint wall hardness were collected and LD profiles were obtained by sequential surface grinding and testing. These natural rock specimens included those with wall surface materials softer and harder relative to the underlying intact rock. A Hardness Contrast Type was proposed for classification of hardness contrast conditions. The study findings showed the LH test is a suitable tool for predicting JCS and a proposed methodology was presented.

Robust mechanical models for shear and punching strength of concrete slabs

AbstractThe mechanical models in this paper are based on the nominal concrete tensile strength that is considered to be the average strength along a 150 mm long brittle failure crack. Fracture Mechanics principles are applied for addressing the size effect of brittle failures; increasing member size leads to decreasing failure stress. The shear force is assumed to be carried by the compression chord of the slab and by aggregate interlock along part of the initial shear crack. Shear failure occurs when the aggregate interlock reaches its failure force that is assumed to be constant for effective depths larger than 1.0 m. The slab outside the initial shear crack around the column in a flat slab is assumed to be suspended into the stable inverted pyramid above the column. Punching failure occurs when the suspension force fails. These failure modes are used for validation of the provisions for shear and punching in the new version of Eurocode 2 (EC2). It is found that EC2 still underestimates the size effect and the code also incorrectly claims that punching failure is related to shear failure. Corrections of these two cases are proposed in the paper. A formula for design of shear reinforcement for flat slabs is proposed where iteration is not required. The paradox that brittle punching failure can cooperate with flexible shear reinforcement is explained. An additional formula is derived that gives a flat slab such large ductility that brittle punching failure can be considered as eliminated.

Hybrid soft computing-based predictive models for shear strength of exterior reinforced concrete beam-column joints

Hybrid soft computing-based predictive models for shear strength of exterior reinforced concrete beam-column joints

Shear capacity prediction of carbon-fibre-reinforced polymer mesh fabric (CFRP-MF) stirrups reinforced concrete beams

Fibre-reinforced polymer mesh fabric (FRP-MF) is an effective substitute for normal steel stirrups in concrete to prevent steel corrosion in reinforced concrete (RC) structures. Nevertheless, methods for calculating the shear resistance of RC members rehabilitated in shear with FRP-MF configurations are lacking. Moreover, the methods for quantitative analysis of the shear contribution caused by the cracking control effect produced by CFRP-MF longitudinal FRP strips are unclear. In this paper, a mechanics-based segmental model is presented based on partial interaction analysis considering both transverse FRP strips and longitudinal reinforcements. By incorporating the carbon-fibre-reinforced polymer mesh fabric (CFRP-MF) stirrups into this model, a shear strength model was proposed and extended for design purposes in a closed form. In addition, the modified compression field theory (MCFT) model considering the shear strength of concrete in compression and the shear friction model (SFM) in the literature were appropriately modified and used for comparison with the segmental model. It should be noted that if the tensile strength of the longitudinal strips was added into the corresponding calculation formulas, it would cause the difference between the proposed novel models with the previous models. Finally, the shear strength of 14 CFRP-MF reinforced concrete beams were evaluated using these three models. The average ratios of the experimental values of the CFRP-MF RC beams to the shear capacity predicted by the modified segmental model, MCFT with the shear contribution of compressed concrete and SFM were 1.30, 1.19 and 1.19, respectively. It turns out that the modified mechanics-based segmental model yields the minimum discreteness based on the experiment results. This study can provide shear capacity calculation and design reference of RC beams with longitudinal FRP strip reinforcements for researchers and engineers.

Shear strength predicting models for steel fiber-reinforced concrete beams without stirrups: a review

ABSTRACT Many experimental and analytical investigations have been conducted in the past to understand the shear behavior of steel fiber-reinforced concrete (SFRC) beams and the factors influencing their shear strength. Various shear strength prediction models have also been developed in the literature, based on different theories and approaches. This paper reviews major methodologies for modeling the shear strength of SFRC beams without stirrups. It highlights that many models rely on complex parameters difficult to measure experimentally, while others, such as regression and machine learning methods, use limited parameters but can yield inconsistent accuracy due to dataset variability. Consequently, there is a need for a shear strength model that relies on straightforward mechanical principles and can predict the shear strength of SFRC beams with greater accuracy, particularly for configurations and loading conditions that have not yet been tested. This review paper aims to outline a path forward and establish a framework for developing an accurate and simple model for predicting the shear strength of SFRC beams without stirrups, taking into account the advantages and disadvantages of existing models while enhancing our understanding of shear transfer mechanisms.

Mechanics guided data-driven model for seismic shear strength of exterior beam-column joints

This study presents an enhanced predictive model for the seismic shear strength of exterior beam-column joints (BCJs). Initially, the principles of strut-and-tie mechanism and variable selection procedures were first utilized to identify the most influential parameters. Subsequently, an evolutionary algorithm, specifically multigene genetic programming (MGGP), was utilized to search for the near-optimal predictive model. The dataset used to develop, train, and test the proposed model was compiled from previously published tests, focusing specifically on cyclically loaded exterior BCJs that encountered shear and flexure -shear failures. The prediction performance of the developed model was assessed through various statistical measures, and then compared with that of other existing models. Additionally, sensitivity analyses were also performed to identify the influence and importance of each design parameter. The results demonstrated that the methodology employed in this study yielded an elegant model that adheres to the underlying mechanics and provides higher prediction accuracy compared to existing models. Furthermore, the sensitivity analyses showed that BCJ shear strength positively correlates with concrete compressive strength, beam reinforcement, joint transverse reinforcement, column intermediate vertical reinforcement, and axial load ratio, while it negatively correlates with the joint aspect ratio. Among these design parameters, beam reinforcement has the greatest influence on the model response, followed by concrete compressive strength. Conversely, column intermediate vertical reinforcement and axial load ratio have the least impact on the model response. The notable prediction capabilities and robustness demonstrated by the developed model render it an efficient design tool with promising potentials for adoption by practicing engineers and for consideration in design guidelines.

Evaluation of shear strength models for ETS-FRP-retrofitted beams subjected to shear-critical failure

This study presents a new shear strength model for reinforced concrete beams strengthened with embedded through-section fiber-reinforced polymer (ETS-FRP) bars, accounting for three potential failure modes: shear compression, shear tension, and shear-balanced failures. The bonding-based analysis for ETS-FRP retrofitting elements is integrated into the developed shear approach. Validation against available test data reveals that the developed model outperforms existing methods in predicting the shear capacity of ETS-FRP-intervened beams. The developed computation technique also demonstrates consistent accuracy across a broad range of design variables and adheres to all upper shear limits in the design codes. To serve practical use, this study provides specific design strategies and perspectives for optimizing ETS-FRP strengthening systems and determining appropriate shear reinforcement amounts for ETS-retrofitted beams.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

Experimental Study on Shear Strengthening of Reinforced Concrete Beams by Fabric-Reinforced Cementitious Matrix.

In this study, an experiment was conducted to investigate the shear performance of reinforced concrete (RC) beams strengthened using fabric-reinforced cementitious matrices (FRCM). Four reinforced concrete beams, including a control specimen, were fabricated, and the shear strengthening effect of the FRCM was investigated on eight shear specimens, with the strengthening type and shear reinforcement as key variables. In particular, the digital image correlation (DIC) technique was applied to closely analyze the deformation of reinforced concrete beams subjected to shear forces. The average shear strain-shear stress curve of each specimen was derived, and the contributions of shear and bending to the vertical deflection and the change in the principal strain angle with increasing shear force were analyzed. The experiment results showed that all specimens failed with diagonal cracks within the shear span. In the specimens without shear reinforcement, the shear strength increased by up to 65% according to the FRCM strengthening, while in the specimens with shear reinforcement, only the sided bond strengthened specimen showed a strength increase of 16% compared to the control specimen. Based on displacement data of the DIC, it was confirmed that FRCM strengthening can control the deformation of the RC beam. To evaluate the shear strength of the FRCM-strengthened RC beams, a shear strength model was proposed by considering the contributions of the concrete section, shear reinforcement, and FRCM. The proposed model was capable of reasonably evaluating the shear strength of RC beams strengthened with FRCM, considering the shear contribution of FRCM and bond capacity between FRCM and concrete substrate, in which the shear strength of specimens was underestimated by 28% to 35%.

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.

Estimation of ultimate bearing capacity of strip footings on cohesive-frictional soils using non-linear shear strength model

This study widely investigated the applicability of the ultimate bearing capacity formula from the Architectural Institute of Japan (AIJ) while considering the effect of footing width. An in-house RPFEM program was developed using a non-linear shear strength model that is sensitive to confining stress. The research focuses on evaluating how footing width and soil properties affect the ultimate bearing capacity of strip footings. Coefficients within the AIJ formula were calculated and compared to those derived from the RPFEM, considering different combinations of cohesive and frictional soil strengths across various footing widths. The RPFEM results closely matched those obtained from the AIJ formula. This suggests that using a non-linear shear strength model can accurately estimate the ultimate bearing capacity of strip footings on cohesive-frictional soils, taking into account the footing width.

Study on the Shear Strength of Loess Solidified by Guar Gum and Basalt Fiber.

Loess is widely distributed in the northwest and other regions, and its unique structural forms such as large pores and strong water sensitivity lead to its collapsibility and collapse, which can easily induce slope instability. Guar gum and basalt fiber are natural green materials. For these reasons, this study investigated the solidification of loess by combining guar gum and basalt fiber and analyzed the impact of the guar gum content, fiber length, and fiber content on the soil shearing strength. Using scanning electron microscopy (SEM), the microstructure of loess was examined, revealing the synergistic solidification mechanism of guar gum and basalt fibers. On this basis, a shear strength model was established through regression analysis with fiber length, guar gum content, and fiber content. The results indicate that adding guar gum and basalt fiber increases soil cohesion, as do fiber length, guar gum content, and fiber content. When the fiber length was 12 mm, the fiber content was 1.00%, and the guar gum content was equal to 0.50%, 0.75%, or 1.00%, the peak strength of the solidified loess increased by 82.80%, 85.90%, and 90.40%, respectively. According to the shear strength model, the predicted and test data of the shear strength of solidified loess are evenly distributed on both sides of parallel lines, indicating a good fit. These findings are theoretically significant and provide practical guidance for loess solidification engineering.

Nanostructure and interfacial mechanical properties of PEG/cellulose nanocomposites studied with molecular dynamics

Our starting hypothesis is that Polyethylene glycol (PEG) can be utilized to mix with the biopolymers for consolidating fiber-reinforced composites without deteriorating their hygro-mechanical properties. The effect of PEG on the shear strength during pull-out of crystalline cellulose (CC) fiber out of an amorphous cellulose matrix is simulated with molecular dynamics. The interfacial shear stress shows a stick-slip behavior and is weakened with increasing moisture content. Shear strength increases at low moisture content, manifesting a slight strengthening of interfacial mechanical property due to cohesive forces exerted by the water molecules. At higher moisture content, shear strength is reduced due to breakage of the hydrogen bonds between CC and matrix by water molecules. When adding PEG, amorphous cellulose around the crystalline fiber is replaced by PEG, forming a mixture with amorphous cellulose. It is found that PEG-treated CC-AC composite maintains its shear strength and the presence of PEG does not deteriorate the dependence of the shear strength on moisture content. A shear strength model based on the number of hydrogen bonds between the fiber and the matrix is developed, which validates our initial hypothesis by unraveling the fundamental mechanisms at play. The model reveals that, although the shear strength per hydrogen bond between the fiber and PEG is lower than the shear strength per hydrogen bond between the fiber and amorphous cellulose, the final shear strength is partly compensated by an increase in the total number of hydrogen bonds with increasing PEG ratio. Since PEG reduces the moisture content in the composite at low relative humidity, PEG treated wood in museum conditions will show enhanced shear strength. The framework is a basis for further investigation of realistic archaeological wood with PEG-treatment.

Characterisation and statistical modelling of shear strength in 12 hardwood timber species from the Congo Basin

Shear strength is a wood property which is fundamental to the design of wood-based products and constructions. This property cannot be predicted at present for lack of sufficient knowledge, mainly because of the large number of timber species that occur in the Congo Basin. The main aim of this study was to provide a preliminary qualification of shearing in Congo Basin timber species, with consideration for its variability. For this purpose, we studied 12 timber species with very different properties, from the least dense to the densest. Their shear strength was determined experimentally using European standards specifications, on the scale of the wood material used. A statistical analysis was conducted. To reduce shear strength variability, the species were assigned to four distinct clusters defined according to FCBA Institute specifications. With a view to developing allowable design stresses to facilitate decision-making, we evaluated the relative goodness-of-fit of five probabilistic shear strength distributions (normal, lognormal, exponential, Weibull 2 parameters and Weibull 3 parameters) that are used in wood-related applications. The results of geometric regression (R2 = 0.81) show that shear strength is well correlated with density. Shear strength can be more reliably predicted with the three-parameter Weibull distribution than with the other distributions. The findings of this study open up new prospects to be considered for the design of wood-based products with regard to shear, when using tropical timber species from the Congo Basin.

Predictive modeling of wide-shallow RC beams shear strength considering stirrups effect using (FEM-ML) approach

This paper presents an analysis and prediction of the shear strength of wide-shallow reinforced concrete beams, utilizing Finite Element Analysis (FEA) and machine learning techniques. The methodology involves validating a detailed Finite Element Model (FEM) against experimental results, conducting a parametric study, and developing three Machine Learning prediction equations. The FEM captures concrete and steel behaviors, including cracking and crushing for concrete and linear isotropic properties for steel reinforcement. Loading and boundary conditions are defined for accuracy and validated against 13 experimental specimens, exhibiting a maximum 8% and 12% difference in loads and deflections, respectively. A parametric study generates a dataset of 77 wide beam configurations, exploring variations in beam widths, concrete strengths, compression rebars, and shear reinforcement. This dataset is used to develop machine learning models, including “Genetic Programming (GP)”, “Evolutionary Polynomial Regression (EPR)”, and “Artificial Neural Network (ANN)”. Comparative analysis reveals GP and EPR models with over 95% correlation, while the ANN model outperforms with 99% accuracy. Sensitivity analysis underscores the significant influence of concrete strength and beam aspect ratio on shear strength. In conclusion, the study demonstrates the potential of FEA and machine learning models to predict shear strength in wide-shallow reinforced concrete beams, providing valuable insights for architectural design and engineering practices and emphasizing the role of concrete strength and beam geometry in shear behavior.