Bridge piers are designed to withstand not only axial loads of superstructures and passing vehicles but also out-of-plane loads such as earthquake excitations and vessel impact loads. Vessel impact on bridge piers can lead to substantial damages or even collapse of bridge structures. An increasing number of vessel collision accidents have been reported in the past decade. A lot of researches have been conducted for predicting barge impact loads and calculating structural responses. However, in practice it is not possible to design bridge structures to resist all levels of barge impact loads. Moreover, with an increasing traffic volume and vessel payload in some waterways, the bridge piers designed according to previous specifications might not be sufficient to resist the current vessel impact loads. Therefore, strengthening existing bridge piers are sometimes necessary for protecting structures from barge impact. Carbon fiber reinforced polymer (CFRP) has been widely used in strengthening reinforced concrete structures under impulsive loadings. It is an effective material which has been proven to be able to increase the flexural strength of structures. In this study, CFRP composites are used to strengthen reinforced concrete piers against barge impact loads. Pendulum impact tests are conducted on scaled pier models. Impact force and pier response with and without CFRP strengthening are compared. The effectiveness of using CFRP strengthening the pier model is observed. In addition, numerical models of the bridge piers are developed and calibrated with experimental results. Parametric simulations of barge impacting on piers with or without CFRP strengthening are carried out. The results show that compared with unstrengthened pier, CFRP composite strengthened bridge pier has a higher impact resistance capacity and hence endures less structural damage under the same barge impact load. The effectiveness of CFRP strengthening with different CFRP thickness, CFRP strength and bond strength between the pier and the CFRP composite are also discussed.
Automatic Malicious Code Classification System through Static Analysis Using Machine Learning
