No abstract

Dynamic tensile and compressive tests are carried out to investigate the effect of strain rates on the dynamic tensile and compressive behaviour of concrete. Results of tests indicate that the tensile and compressive strengths of concrete increase with the loading rate. The initial tangential modulus and the critical strain of concrete in tension are independent of strain rate but those in compression slightly increase with the strain rate. Poisson's ratio of concrete in both tension and compression is not obviously dependent of loading rate. The static Drucker-Prager model is modified to the consistency viscoplastic Drucker-Prager model by considering the effect of strain rate. The simulation results using the proposed model show that the present model can simulate the uniaxial dynamic tension and compression behaviour of concrete well. Finally, the dynamic responses of a concrete beam under impact loading are analysed to investigate the influence of strain rates on dynamic responses of concrete structures.

Reported herein are the results of dynamic compressive tests of plain concrete specimens that were carried out with strain rates ranging from 10−5/s to 10−2/s. It is found that the compressive strength and the initial elastic modulus increase but the critical strain of concrete decreases with the increasing strain rate. The damage is defined as a reduction in the tangent modulus. The damage behaviours of concrete in the stress and strain space are investigated. Dynamic compressive load histories with different strain rates and different magnitudes are conducted on plain concrete specimens, and then the dynamic compressive damage tests of these specimens are also conducted. The effects of load histories on dynamic strain–strain curves, dynamic compressive strengths and the dynamic damage behaviour of concrete are studied.

For leakage identification in water distribution networks, if each node is used as a category label of the classifier model, the accuracy of the classifier model will be low because of similar leakage characteristics. By clustering the nodes with similar leakage characteristics and using all the possible combinations of leakages as the category labels of the classifier model, the accuracy of the classifier model for leakage location can be improved. An iterative method combining k-means clustering with the random forest classifier is proposed to identify the leakage zones. In each iteration, k-means clustering is used to divide the leakage zone identified in the previous iterations into two zones, and then, the random forest classifier is used to identify the leakage zones and the number of leakages in each leakage zone. As the number of iterations increases, the number of candidate leakage zones and sensors that conduct leakage zone identification decreases. Thus, feature selection can be used in each iteration to select the minimum number of sensors for model training without affecting identification accuracy. Three leakage scenarios are considered: a single leakage, two simultaneous leakages, and four simultaneous leakages. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. The influences of the number of pressure sensors and Gaussian noise level on the identification results are also discussed. Results indicate that the proposed method is effective for identifying simultaneous leakages.