Estimation Of Shear Strength Research Articles

Shear Strength Estimation of Nonseismically Designed Exterior Beam–Column Joints Strengthened with Unsymmetrical Chamfers

Shear Strength Estimation of Nonseismically Designed Exterior Beam–Column Joints Strengthened with Unsymmetrical Chamfers

Characterization of different longitudinal shear transfer mechanisms for profiles embedded in concrete walls

Concrete walls or columns reinforced by one or multiple embedded steel profiles have gained popularity due to their improved strength, ductility and energy dissipation capacity. However, certain gaps in information are remaining in the available standards regarding the design of such non-conventional reinforced concrete systems. In particular, a proper characterization of the longitudinal shear transfer properties at the steel-concrete interface is required for a reliable design. Although sufficient information is available regarding mechanical connectors like shear studs, detailed information is required for any other types of mechanical connectors such as welded steel plates or for configuration without mechanical connectors. Moreover, even for cases with mechanical connectors, the orientation of the profile – and hence the distance to the face of the concrete – and the tying system can have a significant influence on the load transfer mechanisms. To this purpose, this article presents the outcomes of a set of push-out tests with the objective of comparing the force transfer mechanisms from the steel profile to the surrounding concrete wall for different types of interfaces. 13 tests specimens are investigated, with flexible (shear studs) and/or rigid (steel plates) shear connectors, considering different orientations of the profile and tying mechanisms, as well as a comparison with profiles without mechanical connectors. Based on the test results and subsequent analytical assessment, relevant conclusions are drawn regarding the longitudinal shear transfer at the steel-concrete interface. If necessary provisions are followed, welded steel plates prove to be an effective alternative to the shear stud connectors in terms of connector strength. The orientation of the embedded steel profile, consequent position of the mechanical connectors and their distance to the concrete face are observed to have a significant influence on the compression strut evolution, which therefore dictates the necessity (or not) of horizontal confinement or ties. Furthermore, combining the shear strength offered by different types of mechanical connectors and the steel-concrete bond offer a precise estimate of the longitudinal shear strength and therefore indicates towards the conservative nature of the design provisions suggested by the available standards.

Efficiency of shear connectors under combined shear and torsion

The connection of steel elements to concrete is typically achieved through embedded shear connectors (anchors) placed at the interface of concrete and steel. These shear connectors come in various types, such as headed studs, welded or coupled rebars, shear lugs, or a combination of these, designed to handle large shear forces. Shear connectors primarily resist shear forces and bending loads. However, when the load is not applied along the centroid of the shear connector, it generates a torsional moment in addition to shear- and flexure-induced moments, impacting the performance of shear connectors. To address this phenomenon, this experimental study assesses the behavior of shear connectors, including studs, shear lugs, and rebars, both individually and in combination, under three different load eccentricities relative to anchors spacing connectors (0, 0.25, 0.50, and 0.75). The results are then compared with the control specimen (without eccentricity). The findings revealed that vertical shear lugs can significantly enhance the shear capacity, and ACI 318-19 is conservative in this regard by 41.5% for a relative load eccentricity of 0.75. Nonetheless, it gives good estimations of the shear capacity of connectors without vertical shear lugs. HIGHLIGHTS Contributing to the very limited data on the performance of anchors under the combined action of shear and torsion. ACI 318-19 [1] provides reliable estimations of shear strength for studs/rebars, both with and without eccentricity. However, it tends to be conservative by 41.5% (for a relative eccentricity of 0.75) when horizontal and vertical shear lugs are utilized.

Theoretical method for predicting the failure mode of reinforced concrete shear walls subjected to axial tension and cyclic lateral loads

Reinforced concrete shear walls may exhibit various failure modes when subjected to combined axial tension-flexure-shear loads. This study developed a theoretical method for predicting the failure mode of RC walls subjected to combined axial tension and cyclic lateral loads, in which the force-displacement response curve was compared to the predicted flexural, shear and sliding strength capacity curves. The formulas for calculating the axial elongation and horizontal crack widths of tensile RC walls were proposed, and the influence of these two aspects was fully considered in the estimation of shear strength and sliding strength degradation. The proposed method was validated against test data of ten RC wall specimens which underwent various failure modes, including flexural, shear, sliding, flexural-sliding, and shear-sliding failure modes. Comparisons between experimental and theoretical results indicated that the proposed method predicted the failure modes and lateral strength with acceptable accuracy. As the proposed method accurately estimated the shear and sliding strength degradation, it provided the insights on the shifting of dominated behaviors (e.g., flexural- or shear-dominated behavior to sliding-dominated behavior). Finally, strength design formulas specified in Chinese, US and European codes were verified against test data. The flexural strength of RC walls was reasonably estimated by the JGJ 3–2010 (Chinese code), ACI 318–19 (US code) and Eurocode 8 (European code) design formulas. Among the three codes, JGJ 3–2010 provided a more reasonable estimate on shear strength of tensile RC walls than the other two codes.

Applying Ai Techniques To Analyze Soil Data And Improve Foundation Design In Construction In Iraq

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

Investigation of the Influence of Roughness on the Shear Resistance of Concrete-Rock Interfaces Using Random Field Simulations, Numerical Simulations, and Neural Network Modeling: Proposition of Two Approaches for the Estimation of the Peak Shear Strength

Investigation of the Influence of Roughness on the Shear Resistance of Concrete-Rock Interfaces Using Random Field Simulations, Numerical Simulations, and Neural Network Modeling: Proposition of Two Approaches for the Estimation of the Peak Shear Strength

Comparison of analytical models for shear strength estimation of steel plate girders

Web shear buckling of steel plate girders limits their load-carrying capacity in bending. Several analytical models have been suggested in the literature to estimate the shear capacity of plate girders. This paper presents a critical evaluation of several of these analytical models using the data of experimentally tested plate girders available in the literature. It was found that these analytical models make a conservative estimation of critical buckling strength for plate girders with larger slenderness and/or aspect ratios. Although the predicted ultimate shear strength varied across the different analytical models, no particular trend was identified to show that the aspect and/or slenderness ratios influenced the shear strength. A parametrically conducted analysis indicated that the threshold slenderness ratio (to cause buckling in the web panel) decreases with increasing yield strength and aspect ratio. The paper proposes simplified guidelines for the preliminary sizing of steel plate girders by avoiding shear buckling. It has been shown that the sizes of plate girders determined using the proposed guidelines satisfy design requirements for both flexure and shear.

A Novel Method for Estimating the Undrained Shear Strength of Marine Soil Based on CPTU Tests

The undrained shear strength is an essential parameter in the foundation design of marine structures. Due to the complex marine environment and technical limitations, it is difficult and costly to obtain offshore samples. Piezocone penetration tests (CPTU) are relatively low-cost compared to drilling and sampling methods. Therefore, based on the soil behavior type index (Ic) derived from CPTU results, a model for estimating cone factors (Nkt, Nke) is proposed to improve the accuracy of estimation of undrained shear strength. The result shows that the soil behavior type index (Ic) and cone factors take on a negatively correlated exponential relation. Incorporating a cone factor that varies with the soil behavior type index (Ic) significantly enhances the accuracy of undrained shear strength predictions compared to the conventional method of using a constant cone factor. This approach reduces the root mean square error (RMSE) for Nkt (Nke) from 0.124 (0.126) MPa to 0.056 (0.06) MPa, and the mean absolute error (MAE) from 0.0154 (0.016) MPa to 0.0032 (0.0036) MPa. The method was validated at an additional location and the predictions were in high agreement with the results of the consolidated quick direct shear test. The developed method can serve as an effective tool used in the design of foundations of marine structures.

Undrained shear strength estimation through instrumented free-fall penetration tests using spherical and conical penetrometers

This paper presents (i) results from a series of instrumented free-fall penetration (FFP) tests performed, under laboratory-controlled conditions, in a large-scale testing facility and (ii) a simple methodology to predict undrained shear strength su from FFP test data without resorting to assumptions on the values of different parameters. Comparison of soil resistance derived from acceleration data recorded during FFP tests with soil resistance recorded during constant rate penetration (CRP) tests using a ball penetrometer reveals unique relationships between dynamic and constant-rate penetration resistances for different shapes of FFP tools. Based on regression analysis, expressions are proposed for calculation of resistance ratio ξr for three different shapes of FFP tool. The proposed expressions for ξr further facilitate the prediction of undrained shear strength su employing acceleration data recorded during FFP tests. FFP-based predictions of su show excellent agreement with CRP-based su prediction in three different laboratory testbeds. Furthermore, the proposed methodology successfully predicts su profile at a field test site using FFP test data reported in the literature.

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

SummaryPrecise estimation of the shear strength of concrete‐filled steel tubes (CFSTs) is a crucial requirement for the design of these members. The existing design codes and empirical equations are inconsistent in predicting the shear strength of these members. This paper provides a data‐driven approach for the shear strength estimation of circular CFSTs. For this purpose, the authors evaluated and compared the performance of nine machine learning (ML) methods, namely linear regression, decision tree (DT), k‐nearest neighbors (KNN), support vector regression (SVR), random forest (RF), bagging regression (BR), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) in estimating the shear strength of CFSTs on an experimental database compiled from the results of 230 shear tests on CFSTs in the literature. For each model, hyperparameter tuning was performed by conducting a grid search in combination with k‐fold cross‐validation (CV). Comparing the nine methods in terms of several performance measures showed that the XGBoost model was the most accurate in predicting the shear strength of CFSTs. This model also showed superior accuracy in predicting the shear strength of CFSTs when compared to the formulas provided in design codes and the existing empirical equations. The Shapley Additive exPlanations (SHAP) technique was also used to interpret the results of the XGBoost model. Using SHAP, the features with the greatest impact on the shear strength of CFSTs were found to be the cross‐sectional area of the steel tube, the axial load ratio, and the shear span ratio, in that order.

A prediction model for residual shear strength of liquefiable soil based on lateral spreading cases

The residual shear strength of liquefiable soil plays an important role in evaluating the displacement magnitude of lateral spreading when applying the Newmark sliding block method. Currently, the empirical model is a mainstream alternative method for estimating the residual shear strength. In this study, a database of well-documented lateral spreading cases is used to establish an exponential function for residual shear strength evaluation correlated with the vertical effective stress and normalized standard penetration test blow counts for liquefied soil. A comparative analysis with two other empirical models is conducted, and the lateral spreading of the Wildlife Site during the Superstition Hills earthquake in 1987 is estimated using the Newmark sliding block method. The model exhibits good performance for soil residual shear strength estimation in cases of liquefaction-induced lateral spreading. The average estimated value of the residual shear strength may be appropriate for deterministic analyses in engineering practice.

Machine Learning-Based Framework for Failure Forecast and Shear Strength Estimation of Non-Conforming RC Shear Walls

ABSTRACT Earthquake reconnaissance after the recent Kahramanmaras, Turkey, earthquake sequence in February 6, 2023 has shown that the majority of the reinforced concrete (RC) structural elements do not comply with the Turkish Building Earthquake Code (TBEC 2018) requirements. Accurate estimation of the actual strength of existing non-compliant RC structural members is critical to accomplish reliable earthquake performance assessments. This study aims to propose a machine learning-based framework to establish a realistic failure forecast as well as an accurate shear strength estimation depending on the expected failure modes of such walls. To achieve this, shear wall design properties are assigned as inputs whereas failure mode and shear strength were designated as outputs for the corresponding classification and regression problems, respectively. Widely used machine learning methods, namely: Ridge Linear Regression, Logistic Regression, Multi-layer Perceptron, Support Vector Machine, Random Forest, XGBoost, LightGBM, and CatBoost are employed using a database consisting of 432 non-conforming shear walls. The data is randomly split to train and test sets (80% and 20%, respectively) to develop the predictive models and to evaluate model performances, respectively. Performance metrics (e.g. R 2 , RMSE) are evaluated over a hundred random splits to ensure robustness. The proposed CatBoost-based models achieve overall accuracies above 90% for both failure prediction and shear strength estimation. The proposed framework is believed to be a promising tool for earthquake engineers for practical and accurate failure mode forecast and shear strength estimation of non-compliant RC shear walls.

Joint shear strength prediction for fiber reinforced concrete beam-to-column connections

Beam-to-column joints experience substantial shear forces and deformations, playing a pivotal role in maintaining the overall stability and integrity of moment-resisting frames under earthquake loading. If fiber reinforced concrete is used instead of reinforced concrete, beam flexural failure can be achieved, which leads to a ductile behavior and increased energy dissipation capacity, when compared to joint shear failure. To ensure a ductile failure mode, accurate prediction of the joint shear strength is essential. However, there are only a limited number of analytical studies on predicting the shear strength of fiber reinforced concrete joints, most of which are only applicable to steel fibers. In this study, a comprehensive database of prior experiments conducted on fiber reinforced concrete beam-to-column connection subassemblies is compiled. Based on Pearson Correlation Analyses, the factors influencing the seismic response are determined to be the composite tensile cracking strength, axial load on the column, aspect ratios of member dimensions, and specific fiber properties such as aspect ratio and volume fraction. A joint shear strength prediction equation is then developed utilizing statistical nonlinear regression method. Validation against experimental data confirms that the proposed equation provides conservative estimates of joint shear strength and can be utilized for composites with any fiber type, even hybrid fibers; in contrast to existing equations which often lead to overestimation of strength and restricted to a specific fiber type. Moreover, the proposed equation provides engineers a practical tool to assess the seismic behavior of such connections, which has not been addressed by current structural design codes yet. The improved accuracy of the proposed equation, demonstrated by the lowest mean absolute error and coefficient of variation and the highest coefficient of correlation, positions it as a promising candidate for integration into structural design codes.

Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction

This research presents a comprehensive investigation into the accurate estimation of shear strength in rectangular reinforced concrete columns through advanced machine learning (ML) models. The study addresses the intricate challenge posed by shear strength complexity, which is crucial for evaluating column stability and ensuring structural integrity. Building upon a substantial dataset comprising 545 experimental observations sourced from diverse literature, this research establishes a robust foundation for predictive modeling. Four distinct ML regression models, Random Forest, Decision Tree, XGBoost, and LightGBM, are meticulously evaluated for their performance. The evaluation employs established metrics, including R2, RMSE, MAE, and MAPE to quantify their predictive capabilities. The outcomes highlight the models' robustness in capturing nuanced variations in shear strength, with impressive R2 values ranging from 93.6% to 93.9%, showcasing their exceptional ability to elucidate intricate shear behaviors. Furthermore, comparative analysis indicates the slightly superior performance of the Random Forest over the Decision Tree, highlighting the efficacy of ensemble methods in this context. Extending the exploration to include XGBoost and LightGBM, the study showcases their potential as accurate shear strength predictors. The performance of the models is validated through scatter plots and error distribution plots, confirming accurate shear strength predictions across various scenarios. This research contributes significantly to the advancement of structural engineering methodologies by highlighting the potential of ML to improve the accuracy of shear strength estimation. The findings not only underscore the exceptional performance of ML models but also provide valuable insights into their comparative effectiveness, paving the way for enhanced structural assessments in columns.

Determination of the overconsolidation ratio and undrained shear strength of cohesive soils by CPTu measurement

Estimations of the undrained shear strength (Su) and overconsolition ratio (OCR) of cohesive soil are essential for the evaluation of geology investigation. In comparison to time-consuming laboratory tests, the field piezocone penetration test (CPTu) can rapidly evaluate these parameters. However, existing studies have mainly relied on empirical correlations to estimate the undrained shear strength Su and OCR from CPTu measurements. These correlations are mainly empirical, involving fixed coefficients multiplied by CPTu measurement. Based on the coupled Eulerian-Lagrangian method, this study employed large deformation finite element modeling to summarize and extend the relationships between CPTu measurement and the estimation of undrained shear strength Su and OCR in cohesive soils. These relationships allow for predictions based on CPTu measurements without the requirement for laboratory calibration or referencing to field benchmarks. The accuracy and applicability of the proposed prediction formulas are validated through field case studies for comparative analysis.

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

Determining the joint roughness accurately will better serve the peak shear strength estimation models of rock joints used for stability assessment of rock masses. Considering the defects of the existing quantitative characterization parameters for two-dimensional (2D) joint roughness, especially the lack of explicit geometric and physical meaning, we proposed two new 2D roughness parameters, θ2D and h2D. The former, θ2D, represents the average inclination angle of all potential contact asperities over the entire joint profile, while the latter, h2D, characterizes their average undulation height. Both parameters are closely related to the shear strength of rock joints. Then, the roughness parameters θ2D and h2D of 102 rock joint profiles digitized at 0.5 mm sampling interval were calculated, and a new nonlinear regression equation for the determination of the 2D joint roughness coefficient (JRC) was established by combining the calculated results of the two roughness parameters. It was verified that the proposed equation could give accurate JRC estimation values of the 10 standard profiles of rock joints. Through the comparative analysis of the experimental data collected from earlier studies for the peak shear strength of 73 rock joint samples and corresponding estimated values, the equation was further verified to be applicable and accurate for estimating the JRC values of rock joints. Furthermore, we discussed the effects of shear direction and sampling interval on roughness and further provided another equation that could be applied to estimate the JRC values of joint profiles at the sampling interval of 1.0 mm.

Resistência à punção de lajes lisas com armadura de cisalhamento: considerações sobre os perímetros de controle

Abstract In structural design practice and scientific research on flat slabs, estimating the punching shear resistance of slab-column connection with shear reinforcement involves checking the shear stress acting on a control perimeter outside the shear-reinforced zone. Depending on the arrangement chosen for the shear reinforcement, the design codes` recommendations can lead to different interpretations of the geometry of the external control perimeter, which are, in some cases, also complex to formulate into closed-form equations. Thus, it is common that, especially in hand-made estimates, the slab-column connections are drawn, and dimensions of the external control perimeters are individually measured and applied to the design equations. This paper presents equations developed for ABNT NBR 6118, Eurocode 2 and ACI 318 to account for the length of the control perimeter outside the shear-reinforced zone, allowing the automated calculation for different shear reinforcement arrangements. The different interpretations of the geometry of the external control perimeter of these design codes are discussed and clarified. The proposed equations were validated by comparing their results with those obtained from individual drawing analysis of 62 slab-column connections with varied geometries and shear reinforcement arrangements, and the maximum error was up to 0.1%. This paper significantly contributes to the automation and optimisation of the punching shear strength estimates of slab-column connections with shear reinforcement, clarifying obscure points and automating the definition of the geometry of the external control perimeter.

Shear strength estimation of a FRP-strengthened RC beam: A comparison between an artificial neural network and guideline equations

Shear strength estimation of a FRP-strengthened RC beam: A comparison between an artificial neural network and guideline equations

Seismic behavior of eccentric new RCS joints with through columns

Architectural requirements often result in beam-column joints with eccentrically framing beams. However, the fabrication of eccentric through beam-type reinforced concrete column and steel beam (RCS) joint is extremely complex due to the presence of column longitudinal reinforcement and joint confinement reinforcement. To mitigate the constructability issues associated with through-beam type RCS eccentric joints, the current study proposed a through-column type New RCS joint. The proposed New RCS joint used joint cover plates and continuity plates as additional attachments while eliminating the need for joint confinement reinforcement. Two large-scale beam column subassembly specimens were tested to evaluate the seismic behavior. Both specimens had high-strength reinforced concrete columns (New RC columns) with a design concrete grade of 84 MPa and steel reinforcement of specified yield stress 690 MPa. The experimental results revealed the asymmetric shear stress development in the joint cover plates due to joint eccentricity. Based on experimental observations, a joint shear force-deformation model was presented, which was able to capture the joint shear response with reasonable accuracy. Finally, simplified expressions were presented, which were able to provide reliable estimates of joint shear strength, which is suitable for design purposes.