The compression chord capacity model for the shear design and assessment of reinforced and prestressed concrete beams

This is the accepted version of the following article: [Cladera, A., Marí, A., Bairán, J. M., Ribas, C., Oller, E. and Duarte, N. (2016), The compression chord capacity model for the shear design and assessment of reinforced and prestressed concrete beams. Structural Concrete, 17: 1017–1032. doi:10.1002/suco.201500214], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/suco.201500214/fullA simplified mechanical model is presented for the shear strength prediction of reinforced and prestressed concrete members with and without transverse reinforcement, with I, T or rectangular cross-section. The model, derived with further simplifications from a previous one developed by the authors, incorporates the contributions of the concrete compression chord, the cracked web, the dowel action and the shear reinforcement in a compact formulation. The mechanical character of the model provides valuable information about the physics of the problem and incorporates the most relevant parameters governing the shear strength of structural concrete members. The predictions of the model fit very well the experimental results collected in the ACI-DAfStb databases of shear tests on slender reinforced and prestressed concrete beams with and without stirrups. Due to this fact and the simplicity of the derived equations it may become a very useful tool for structural design and assessment in engineering practice.Peer ReviewedPostprint (author's final draft

A Feasibility Study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrups.

yesShear failure of concrete elements reinforced with Fiber Reinforced Polymer (FRP) bars is generally brittle, requiring accurate predictions to avoid it. In the last decade, a variety of artificial intelligence based approaches have been successfully applied to predict the shear capacity of FRP Reinforced Concrete (FRP-RC). In this paper, a new approach, namely, biogeography-based programming (BBP) is introduced for predicting the shear capacity of FRP-RC beams based on test results available in the literature. The performance of the BBP model is compared with several shear design equations, two previously developed artificial intelligence models and experimental results. It was found that the proposed model provides the most accurate results in calculating the shear capacity of FRP-RC beams among the considered shear capacity models. The proposed BBP model can also correctly predict the trend of different influencing variables on the shear capacity of FRP-RC beams

Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups

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.National University of Science and Technolog

Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework

Methods for predicting the shear capacity of FRP shear strengthened RC beams assume the traditional approach of superimposing the contribution of the FRP reinforcing to the contributions from the reinforcing steel and the concrete. These methods become the basis for most guides for the design of externally bonded FRP systems for strengthening concrete structures. The variations among them come from the way they account for the effect of basic shear design parameters on shear capacity. This paper presents a simple method for defining improved equations to calculate the shear capacity of reinforced concrete beams externally shear strengthened with FRP. For the first time, the equations are obtained in a multiobjective optimization framework solved by using genetic algorithms, resulting from considering simultaneously the experimental results of beams with and without FRP external reinforcement. The performance of the new proposed equations is compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. The proposed procedure is also reformulated as a constrained optimization problem to provide more conservative shear predictions

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Shear failure in reinforced concrete (RC) beams with a brittle nature is a serious safety concern. Due to the inadequate description of the phenomenology of shear resistance (the shear behavior of RC beams), several of the existing shear design equations for RC beams with stirrups have high uncertainty. Therefore, the predicted models with higher accuracy and lower variability are critical for the shear design of RC beams with stirrups. To predict the ultimate shear strength of RC beams with stirrups, machine learning (ML)-based models are proposed in the present research. The models were created using a database of 201 experimental RC beams with stirrups gathered from earlier investigations for training and testing of the ML method, with 70% of the data being used for model training and the rest for testing. The performance of suggested models was evaluated using statistical comparisons between experimental results and state-of-the-art current shear design models (ACI 318–08, Canadian code, GB 510010–2010, NZS 3101, BNBC 2015). The suggested machine learning-based models are consistent with experimentally observed shear strength and current predictive models, but they are more accurate and impartial. To understand the model very well, sensitivity analysis is determining as input values for a specific variable affect the outcomes of a mathematical model. To compare the results with different machine learning models in training and testing R2 , RMSE and MSE are also established. Finally, proposed ML models such as gradient boost regressor and random forest give higher accuracy to evaluate the shear strength of the reinforcement concrete beam using stirrups.Md Nasir Uddin, Kequan Yu, Ling, zhi Li, Junhong Ye, T. Tafsirojjaman, Wael Alhadda

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength

Modelo mecánico para la resistencia a cortante de vigas de hormigón armado reforzado con fibras, sin armadura de cortante

Despite the numerous studies made showing that the addition of steel fibres to concrete enhances the shear strength of RC beams, current design formulations are still empirical and present large scatter in front of the test results. In this paper, the previously developed Multi-Action Shear Model is extended to SFRC beams without stirrups, adopting an analytical formulation to evaluate the residual tensile stress of SFRC and incorporating the effects of the crack bridging capacity of SFRC in the shear resisted trough the different shear-transfer mechanisms. The proposed model predicts the tests results included in a recently published database with 448 shear tests with less scatter than any of the existing models.The financial support provided by the University of Messina (Italy), through the scholarship granted for a two-months research and teaching stage of the first author, is acknowledged. This work is part of the Research Projects BIA2015-64672-C4-1-R, funded by the Spanish Ministry of Economy and competitiveness, and RTI2018-097314-B-C21, funded by the Spanish Ministry of Science and Innovation.Postprint (published version

Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning

Shear failure in reinforced concrete beams poses a critical safety issue since it may occur without any prior signs of damage in some cases. Many of the existing shear design equations for steel fiber reinforced concrete (SFRC) beams include significant uncertainty due to failure in reflecting the phenomenology of shear resistance accurately. Given these, adequate reliability evaluation of shear design provisions for SFRC beam is of high significance, and increased accuracy and minimisation of variability in the predictive model is essential. This contribution proposes machine learning (ML) based methods - Gaussian Process regression (GPR) and the Random Forest (RF) techniques - to predict the ultimate shear resistance of SFRC slender beams without stirrups. The models were developed using a database of 326 experimental SFRC slender beams obtained from previous studies, utilising 75% for model training and the remainder for testing. The performance of the proposed models was assessed by statistical comparison to experimental results and to that of the state-of-practice existing shear design models (fib Model Code 2010, German guideline, Bernat et al. model). The proposed ML-based models are in close alignment with the experimentally observed shear strength and the existing predictive models, but provide more accurate and unbiased predictions. Furthermore, the model uncertainty of the various resistance models was characterised and investigated. The ML-based models displayed the lowest bias and variability, with no significant trend with input parameters. The inconsistencies observed in the predictions by the existing shear design formulations at the variation of shear span to effective depth ratio is a major cause for concern; reliability analysis is required. Finally, partial resistance safety factors were proposed for the model uncertainty associated with the existing shear design equations

Study on the shear and punching-shear strength of reinforced concrete slabs subjected to point loads and in-plane tensile forces

Reinforced concrete members, especially when subjected to concentrated loads, can fail in shear. This is an undesirable and brittle failure mode preventing the structure from deforming and reaching higher load levels. It is therefore important to investigate and understand the nature of shear failures in RC beams and slabs, which are typically not provided with shear reinforcement. Despite the exhaustive research work carried out since the beginning of the XXth century, the shear behavior of reinforced concrete elements is still not fully clear. The complex kinematics and the different contribution of the widely accepted shear resisting actions, which depend, among other parameters, on the load level and the geometry of the specimens, are part of those uncertainties that have provoked the lack of consensus that currently exist around the shear problem in structural engineering. In this context, this thesis focuses on the shear behavior of reinforced concrete slabs without shear reinforcement subjected to point loads. On the one hand, part of the work carried out during this investigation deals with shear in one way slabs supported on linear supports. Thanks to the experimental work conducted in the last two decades, a significant database of test results is now available, which has allowed to develop and verify a new mechanical model to predict the shear strength of RC one-way slabs without shear reinforcement. It takes into account a significant number of variables involved in the phenomenon and is applicable to is applicable to simply supported slabs, cantilever slabs and situations with partial restraint to the rotation. The model is divided into two sub-models. One for loads applied close to supports, where the direct transmission of the load to the support plays an important role in the shear strength and a second one for loads applied away from supports where the possibility of a shear failure is assumed to coexist with the possibility of a local punching failure. On the other hand, the second part of the investigation deals with the shear behavior of reinforced concrete slabs subjected to the simultaneous action of in plane tensile stresses and out-of-plane point loads. This load case has not been exhaustively studied throughout the years, and in order to contribute to gain insight into this aspects of shear design, the mechanical model recently developed at UPC for the prediction of the punching-shear strength of two-way slabs has been extended to the case of simultaneous in-plane tensile forces. A set of experimental test for the validation of the model for the particular case of uniaxial in-plane tension is also presented. In addition to that, the particular case of one-way simply supported slabs subjected to transverse tensile stresses has also been experimentally studied. This is a seldom studied load case and the conducted set of test will help to understand the overall behavior of these elements under this particular loading condition.Los elementos de hormigón armado, especialmente cuando están sometidos a la acción de cargas puntuales, pueden alcanzar su resistencia última a esfuerzo cortante, lo cual limita su capacidad para deformarse y resistir valores de carga mayores. Este es un modo de fallo frágil y repentino, por lo que debe ser evitado. Así pues, es importante seguir investigando y comprender la naturaleza de la respuesta de vigas y losas de hormigón armado ante solicitaciones de este tipo, sobre todo cuando no están provistas de armadura de cortante. A pesar de la gran cantidad de campañas experimentales llevadas a cabo desde principios del siglo XX, el comportamiento a cortante y punzonamiento de los elementos estructurales de hormigón armado no está todavía del todo claro. La compleja cinemática y la diferente contribución de los mecanismos resistentes ampliamente aceptados, la cual depende, entre otros factores, del nivel de carga y de la geometría de los especímenes, son parte de esas incertidumbres que han provocado la falta de consenso que existe actualmente dentro del campo de la ingeniería estructural. En este contexto, esta tesis se centra en el comportamiento a cortante de losas de hormigón armado sin armadura de cortante sometidas a la acción de cargas puntuales. Por un lado, parte del trabajo realizado durante esta investigación está orientado al cortante en losas unidireccionales apoyadas sobre apoyos lineales. Gracias a todas las campañas experimentales realizadas en las últimas dos décadas, una significativa base de datos de resultados experimentales se encuentra disponible en la literatura, lo que ha permitido desarrollar y verificar un nuevo modelo mecánico para predecir la resistencia última a cortante de este tipo de elementos. Dicho modelo tiene en cuenta un número importante de variables involucradas en el fenómeno y es aplicable a losas simplemente apoyadas, en ménsula, y apoyadas en apoyos con restricción parcial al giro. El modelo se ha dividido en dos sub-modelos para facilitar su desarrollo. Uno para cargas aplicadas cerca de los apoyos, donde la transmisión directa de la carga al apoyo juega un papel importante, y un segundo para cargas aplicadas lejos de los apoyos, en donde se asume que la posibilidad del fallo por cortante coexiste con la posibilidad de un fallo local por punzonamiento. Por otro lado, la segunda parte de la investigación está focalizada en el comportamiento a cortante de losas de hormigón armado sometidas simultáneamente a la acción de cargas puntuales y tracciones en su plano. Este caso de carga no ha sido demasiado investigado a lo largo de las últimas décadas, y con la intención de contribuir a comprender esta compleja interacción, el modelo mecánico recientemente desarrollado en la UPC, para la predicción de la resistencia a punzonamiento en losas bidireccionales, ha sido extendido a este caso particular de cargas y validado con los resultados de campañas experimentales disponibles en la literatura y el conjunto de ensayos realizados como parte de esta investigación para el caso particular de tracciones unidireccionales. Adicionalmente, el efecto de las tracciones transversales en el plano en la resistencia a cortante de losas unidireccionales también ha sido estudiado experimentalmente. Esta combinación de cargas no ha sido apenas investigada y un nuevo grupo de ensayos experimentales puede contribuir a comprender el comportamiento global de este tipo de elementos ante este caso particular de carga.Postprint (published version