인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
초록·키워드
Recently, the transportation of dangerous explosive goods is increasing, which makes vehicle blasting accidents a potential threat for the safety of bridge structures. In addition, blasting accidents happen more easily when earthquake occurs. Excessive dynamic response of bridges under extreme loads may cause local member damage, serviceability issues, or even failure of the whole structure. In this paper, a new explosion-proof and aseismic system is proposed including cable support damping bearing and steel-fiber reinforced concrete based on the existing researches. Then, considering one 40m-span simply supported concrete T-bridge as the prototype, through scale model test and numerical simulation, the dynamic response of the bridge under three conditions including only earthquake, only blast load and the combination of the two extreme loads is obtained and the applicability of this explosion-proof and aseismic system is explored. Results of the study show that this explosion-proof and aseismic system has good adaptability to seism and blast load at different level. The reducing vibration isolation efficiency of cable support damping bearing is pretty high. Increasing cables does not affect the good shock-absorption performance of the original bearing. The new system is good at shock absorption and displacement limitation. It works well in reducing the vertical dynamic response of beam body, and could limit the relative displacement between main girder and capping beam in different orientation so as to solve the problem of beam falling. The study also shows that the enhancement of steel fibers in concrete could significantly improve the blast resistance of main beam. Results of this paper can be used in the process of antiknock design, and provide strong theoretical basis for comprehensive protection and support of girder bridges.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.