A progressive collapse evaluation of steel structures in high temperature environment with optical fiber sensors

In the process of a progressive failure of steel structures in a post-earthquake fire, real-time assessment and prediction of structural behaviors are of paramount significance to an emergency evacuation and rescue effort. However, existing measurement technologies cannot provide the needed critical data such as large strains at high temperature. To bridge this gap, a novel optical fiber sensor network and an adaptive multi-scale finite element model (FEM) are proposed and developed in this study. The sensor network consists of long period fiber gratings (LPFG) sensors and extrinsic Fabry-Perot interferometer (EFPI) sensors or their integration. Each sensor is designed with a three-tier structure for an accurate and reliable measurement of large strains and for ease of installation. To maintain a balance between the total cost of computation and instrumentation and the accuracy in numerical simulation, a structure is divided into representative/critical components instrumented densely and the remaining components simulated computationally. The critical components and the remaining were modeled in different scales with fiber elements and beam/plate elements, respectively, so that the material behavior and load information measured from the critical components are representative to the remaining components and can be used to update the temperature distribution of the structure in real time. Sensitivity studies on the number of sensors and the initial selection of an updating temperature parameter were conducted. Both the sensor network and the FEM were validated with laboratory tests of a single-bay, one-story steel frame under simulated post-earthquake fire conditions. The validated FEM was applied to a two-bay, four-story steel building under the 1995 Kobe earthquake excitations. Based on extensive tests and analyses, the proposed sensor can measure a strain of 12% at as high as 800 °C (1472 °F) in temperature. Within the application range, the LPFG wavelength and the EFPI gap change linearly with the applied strain and temperature. The proposed updating criterion and algorithm in the adaptive FEM are proven to be effective. The number of sensors is sufficient in engineering applications as long as the sensors can adequately represent the material behavior of the instrumented components. The predicted structural behavior is unaffected by any change in a low temperature range and thus insensitive to the initial selection of the updating parameter.