Assessment of shear capacity of adhesive anchors for structures using neural network based model

Abstract

In this study, an artificial neural network (NN) based explicit formulation for predicting the edge breakout shear capacity of single adhesive anchors post-installed into concrete member was proposed. To this aim, a comprehensive experimental database of 98 specimens tested in shear was used to train and test NN model as well as to assess the accuracy of the existing equations given by American Concrete Institute and prestressed/precast concrete Institute. Moreover, the proposed NN model was compared with another existing model which had been derived from gene expression programming by the authors in a previous study. The prediction parameters utilized for derivation of the model were anchor diameter, type of anchor, edge distance, embedment depth, clear clearance of the anchor, type of chemical adhesive, method of injection of the chemical, and compressive strength of the concrete. The proposed model yielded correlation coefficients of 0.983 and 0.984 for training and testing data sets, respectively. It was found that the predictions obtained from NN agreed well with experimental observations, yielding approximately 5 % mean absolute percent error.Moreover, in comparison to the existing models, the proposed NN model had all of the predicted values in ±20 % error bands while the others estimated up to %160 error.

Metodologías empleadas para la producción de concreto permeable usando parcialmente materiales reciclados como agregados: una revisión literaria

“…Pero un estudio de los efectos del estearato de calcio, el oleato de sodio y el metilsiliconato de sodio, aplicados a la absorción de agua, hidratación, resistencia a la compresión, fluidez y periodo de fraguado en la pasta de cemento, dio como resultado que el estearato de calcio es el elemento más adecuado para la preparación de concreto permeable ligero [35]. Al sustituir el agregado natural por AR resultó en un incremento considerable en el coeficiente de permeabilidad, pero las propiedades mecánicas del concreto se ven influenciadas adversamente [36].…”

Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques

“…For anchors failing with a concrete cone failure, the mean value of the calculated to measured values for the testing dataset are 0.99 and 1.63 for the models based on ANN and the CCD method, respectively. A complementary study in [ 22 ] including an analysis by ANN on the same dataset, indicated that an ANN-based model delivered still a higher accuracy with correlation coefficients of 0.983 and 0.984 with the training and testing data, respectively. The fact that the correlation with both the testing and training data is virtually the same, allowed to conclude that the ANN has an overall more reliable prediction performance.…”

Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map

“…Their destruction mechanism can cause extraordinary damage to a structure. In the last few years, much attention has been given to the use of artificial neural networks (ANNs) for solving various civil engineering problems [3][4][5][6][7][8].…”