Progressive-Collapse Simulation and Critical Region Identification of a Stone Arch Bridge

Progressive collapses of arch bridges have repeatedly occurred in recent years, resulting in many casualties and significant property losses. Based on an actual recent and serious progressive collapse of a stone arch bridge, this paper simulated the complete progressive-collapse process using the general purpose finite element (FE) program, MSC.Marc. The simulation adopted a 3D FE model and performed a nonlinear analysis using the contact algorithm in conjunction with the element deactivation technique. The potential causes of the progressive-collapse of the stone arch bridge were also evaluated. Furthermore, the importance of different components of the stone arch bridge was determined with the conception of generalized structural stiffness; thus, the most critical and vulnerable regions of the bridge were identified. The results of the simulated progressive-collapse process agreed well with the actual process, and the predicted critical regions were both correct and realistic. This study also provides important references for the analysis and prevention of progressive collapses of stone arch bridges. CE Database subject headings: Nonlinear analysis; Numerical analysis; Bridge failures; Bridges, arch; Structural failures; Structural safety; Progressive collapse. Author keywords: Stone arch bridge; Progressive-collapse; Computer simulation; Element deactivation; Critical regions. Introduction The arch bridge, a conventional bridge type, is widely used throughout the world and is important in bridge engineering (Yao 2008). However, there has been frequent occurrence of collapses of arch bridges. In the last 14 years, more than 5 progressive collapses of arch 1 Doctoral Candidate, Department of Civil Engineering, Tsinghua University, Beijing 100084, China. 2 Associate Professor, Department of Civil Engineering, Tsinghua University, Beijing 100084, China (corresponding author).Email: [email protected]. 3 Associate Professor, Griffith School of Engineering, Griffith University Gold Coast Campus, Queensland 4222, Australia. 4 Doctoral Candidate, Department of Civil Engineering, Tsinghua University, Beijing 100084, China. 5 Professor, Department of Civil Engineering, Tsinghua University, Beijing 100084, China. 2 bridges have been reported in China, resulting in more than 100 casualties (Chen 2008). Furthermore, amongst the different types of arch bridges, stone arch bridges are the most sensitive to local damages, particularly around the arch ring and arch feet (Zhou et al. 2008). Several typical stone arch bridge collapses are enumerated herein. In 1989, UK’s Ness stone arch bridge in Inverness collapsed due to a flood (Scott 1995). In 1993, the Kenyan stone arch bridge that connected Nairobi and Mombasa collapsed due to a flood and subsequently, a train fell from the bridge, killing 144 people (Scheer 2000). In 2006, China’s Yaohe stone arch bridge collapsed unexpectedly. In 2007, China’s Fenghuang stone arch bridge also collapsed, where 64 people perished (Chen 2008). These catastrophic events have raised an increasing concern for bridge engineers and researchers. To ensure the integrity and safety of stone arch bridges, it is extremely important to analyze the potential causes of progressive collapses and to determine the most critical regions of the bridges, which will in turn provide useful references for bridge design, construction, maintenance and collapse prevention strategies for this important class of bridge structures. The progressive collapse of bridges can be caused by several factors and thus, it is difficult to identify the major causes of a progressive collapse (LeBeau and Wadia-Fascetti 2007; Biezma and Schanack 2007). Published research and engineering case studies have proven that simulating a progressive-collapse process can be an effective method to identify the major factors behind the collapse (Baylot 1997; Farrar and Jauregui 1998; Krauthammer et al. 2002; NIST 2008; NTSB 2008; Poston and West 2005). Different factors that may cause a collapse can be compared via repeated simulations to determine the most sensitive factor. In general, experimental simulations are able to provide more detailed information and are more reliable than numerical methods (Farrar and Jauregui 1998; Poston and West 2005). However, experimental simulations of a bridge collapse are very expensive, time-consuming and labor intensive, particularly when many repeated simulations are required to evaluate the influence of different factors. Differently, numerical simulations are much more efficient and can provide possible solutions to the discovery of causes of bridge collapses.