New insights into the relationship between seismic intensity measures and nonlinear structural response

This paper focuses on the probabilistic analysis of Intensity Measures (IMs) and Engineering Demand Parameters (EDPs) in the context of earthquake-induced ground motions. Several statistical properties, which are desirable in IMs when they are used to predict EDPs, have been analysed. Specifically, efficiency, sufficiency and steadfastness have been quantified for a set of IMs with respect to two EDPs: the maximum inter-storey drift ratio, MIDR, and the maximum floor acceleration, MFA. Steadfastness is a new statistical property proposed in this article, which is related to the ability of IMs to forecast EDPs for large building suites. In other words, this means that efficiency does not significantly vary when different types of buildings are simultanously considered in the statistical analyses. This property allows reducing the number of calculations when performing seismic risk estimations at urban level since, for instance, a large variety of fragility curves, representing specific building typologies, can be grouped together within a more generic one. The main sources of uncertainty involved in the calculation of the seismic risk have been considered in the analysis. To do so, the nonlinear dynamic responses of probabilistic multi-degree-of-freedom building models, subjected to a large data set of ground motion records, have been calculated. These models have been generated to simulate the dynamic behavior of reinforced concrete buildings whose number of stories vary from 3 to 13. 18 spectrum-, energy- and direct-accelerogram-based IMs have been considered herein. Then, from clouds of IM-EDP points, efficiency, sufficiency and steadfastness have been quantified. For MIDR, results show that IMs based on spectral velocity are more efficient and steadfast than the ones based on spectral acceleration; spectral velocity averaged in a range of periods, AvSv, has shown to be the most efficient IM with an adequate level of steadfastness. For MFA, spectral acceleration-based-IMs are more efficient than velocity-based ones. A comparison is also presented on the use of linear vs quadratic regression models, and their implications on the derivation of fragility functions. Concerning sufficiency, most of the 18 IMs analysed do not have this property. Nonetheless, multi-regression models have been employed to address this lack of sufficiency allowing to obtain a so-called ‘ideal’ IM.

Comparing Hysteretic Energy and Ductility Uniform Annual Failure Rate Spectra for Traditional and a Spectral Shape-Based Intensity Measure

In this study, with the objective to develop a reliability-based seismic design tool, ductility and dissipated hysteretic energy uniform annual failure rate (UAFR) spectra are obtained and compared using the spectral acceleration at first mode of vibration of the structure Sa(T1) and the well-known spectral shape-based intensity measure INp. Notice that this is the first time in the literature that UAFR spectra are obtained for the advanced spectral shape intensity measure INp. For this aim, 110 simulated ground motions recorded from the soft soil of Mexico City were selected due to their large energy amount demanded to the structures; moreover, four elastoplastic hysteretic behavior models are considered for the dynamic analyses with post-yielding stiffness of 0, 3, 5, and 10%. It is observed that the use of elasto-perfectly plastic models provided similar UAFR spectra in comparison with hysteretic models with different post-yielding stiffness. This conclusion is valid for the two selected intensity measures. In addition, the lateral resistance required to achieve similar structural reliability levels is larger when the INp intensity measure is used, especially for buildings with vibration periods equal or larger than the soil period, in such a way that the traditional use of Sa(T1) could provide structures with less structural reliability levels.

Mobile home fragility curves

Seismic fragility of mobile (manufactured) homes is investigated. Compiled is a catalog of home performance in past earthquakes. Intensity measures causing damage are characterized by peak ground acceleration and velocity. Damage is defined as when the home is knocked out of position necessitating repairs and re-installation. Four categories of support conditions are identified: unanchored, tie-downs, proprietary systems, and perimeter wall foundations. Suggested fragility curves for unanchored homes and homes with tie-downs are derived from computer simulations. As a benchmark, a fragility curve for proprietary and perimeter wall systems is taken as the same as that for conventional wood homes. Shortcomings of using tie-down and proprietary systems in high seismic zones are discussed. The suggested fragility curves account for the different categories of support conditions thereby representing advancement to those in the Hazus national standardized risk modeling methodology.

Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms

In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.