Probabilistic development of shear strength model for reinforced concrete squat walls

Self Cite

Peak shear strength is a critical parameter in the evaluation of the seismic performance of structural walls. Different equations have been proposed to predict the peak shear strength of reinforced concrete squat walls in literature, which assume lateral loading is parallel to the web. In reality, however, seismic waves can reach structures from any direction, which necessitates the studies on the behavior of structural walls under various lateral loading directions. Unlike rectangular walls, non-rectangular walls naturally possess the capacity to resist lateral loads in both transverse and longitudinal directions. To explore the peak shear strength of such walls under different lateral loading directions, a widely used nonlinear finite element software Diana 9.4 was utilized in this article. Appropriate modeling approaches were first selected and further validated by simulating relevant experiments. Then a comprehensive parametric study was carried out to investigate the influence of lateral loading directions and other important parameters.

Section: Parametric Study On the Peak Shear Strength Of Non-rectangulmentioning

confidence: 99%

Influence of lateral loading direction on the peak shear strength of non-rectangular reinforced concrete squat walls

2019

Self Cite

“…Vu and Hoang (2016) used a hybrid machine learning approach to predict the punching shear capacity of FRP-reinforced concrete slab. However, these models are commonly developed in a deterministic manner (Mansouri et al (2021)), where uncertainties are not included, resulting in a classical confusion, namely, which prediction is the most probable one, exists for engineers (Ning & Li, 2016, 2017, 2018). To solve this problem, the failure-mode–independent peak strength model is developed in a probabilistic manner.…”

Section: Introductionmentioning

confidence: 99%

Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Ning

Wang

2022

Self Cite

A reasonable prediction for the peak strength of reinforced concrete (RC) columns is paramount for the seismic performance evaluation of RC structures. The available prediction models are commonly dependent on the failure mode, and each of them is only applicable to the columns with a particular one. However, the failure mode of RC columns is difficult to be identified accurately in prior, leading to the inconvenience of predicting its peak strength. To overcome this shortcoming, a probabilistic approach was proposed using Bayesian neural network (BNN) to develop a failure-mode–independent model for predicting the peak strength of RC columns directly. The results indicated that the developed model produces reasonable prediction for the peak strength of RC columns failing in different modes. For the training subset, the mean prediction accuracy of the flexure-dominated, flexure-shear-dominated, and shear-dominated columns is 0.997, 0.997, and 0.998, respectively. For the testing subset, the corresponding mean prediction accuracy is 0.957, 0.952, and 0.943. Compared to existing probabilistic models, the developed model exhibits better performance in reducing the uncertainties in peak strength prediction. Compared to existing deterministic models, the developed model could predict the peak strength of RC columns in terms of the confidence interval. In particular, if the confidence interval of peak strength is defined as the mean plus and minus two times standard deviation, 98.9% and 98.4% of the training subset and testing subset are covered. Therefore, the developed model is beneficial for engineers to address the confusion, namely, which peak prediction is the most probable one, when several deterministic models exist for a specific specimen.

“…Therefore, to account for the prevailing uncertainty, the shear strength model of RC beams without shear reinforcement was developed by researchers in a probabilistic manner (Gardoni et al, 2002; Song et al, 2010). Unfortunately, the application of those developed probabilistic models is prevented, especially for users who have little experience on the stochastic modelling method (Ning and Li, 2015, 2016). Therefore, to facilitate the application of the probabilistic model, it is necessary to develop an analytical probabilistic shear strength model with the closed-form expression in terms of a mean prediction model and a standard deviation (STD) prediction model for RC beams without shear reinforcement.…”

Section: Introductionmentioning

confidence: 99%

Analytical probabilistic model for shear strength prediction of reinforced concrete beams without shear reinforcement

Ning

2017

Self Cite

To account for the prevailing uncertainty in shear strength prediction of reinforced concrete beams without shear reinforcement, an analytical probabilistic model in terms of a mean prediction model and a standard deviation prediction model was developed. Specifically, a function form giving the probabilistic representation was first derived with the well-known relationship between the shear forces and the rate of change of bending moments along the beam to reflect the combination of beam action and arch action. Then, two unknown model parameters involved in the developed function form are defined carefully with clear physical meanings as two basic random variables to represent the prevailing uncertainty. After that, the prior distribution ranges of the two unknown model parameters were updated with a constructed experimental database and the generalized likelihood uncertainty estimation method. Moreover, to facilitate the application in engineering practice, the closed-form expression of the developed probabilistic model was derived with the stochastic analysis method to provide a prediction band of shear strength in terms of the mean plus or minus several times standard deviation for each specimen. Performance evaluation demonstrates its advantage, where the measured data and the shear strength predicted by the deterministic models were covered and performed as a quantile of the normal probability density function. Therefore, based on the developed analytical probabilistic model, not only can the reinforced concrete beams without shear reinforcement be designed in confidence but also the prediction accuracy of those deterministic shear strength models can be evaluated in probability for each specimen.