Wang Xiaodan

TL;DR: An intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete by employing the adaptive boosting algorithm to construct a strong learner by integrating several weak learners, which can find the mapping between the input data and output data.

TL;DR: An intelligent approach is presented for FM classification and bearing capacity prediction of RC columns based on the ensemble machine learning techniques and shows that ensemble learning (especially AdaBoost) has better performance than single learning.

TL;DR: In this paper, a beam column joint shear strength prediction method based on a gradient enhanced regression algorithm is proposed, which consists of collecting a large amount of existing frame node core area shear test data; and taking the data as a training set, regarding parameters of various frame beam-column joint test pieces as input variables, taking the shears strength of the beam column joints as an output variable; carrying out multi-round training on the test data through a base learner in a gradient enhancement algorithm; determining weights of different base learners according to the accuracy of a training result

TL;DR: In this article, a concrete material compressive strength prediction method based on the AdaBoost algorithm was proposed, where a large amount of existing concrete compressive-strength test data was collected as a training set; the proportion of each component of the concrete material is regarded as an input variable, and the compressive strengths of concrete material was used as an output variable.

TL;DR: In this paper, a reinforced concrete deep beam bearing capacity evaluation method based on a random forest algorithm was proposed, where a large amount of existing deep flexural member shear bearing capacity data were collected and as a training set.