A computational framework for regional seismic simulation of buildings with multiple fidelity models

Highlights

• A computational framework based on GPU and distributed computing is proposed.

• For high-fidelity models, a GPU solver and a dynamic load balancing strategy are designed.

• For moderate-fidelity models, a parallel method and a static load balancing strategy are designed.

• A virtual city with 50 high-fidelity models and 100,000 moderate-fidelity models is simulated.

• The proposed framework is cost-effective and flexible for regional seismic damage simulation.

Abstract

Regional seismic damage simulation of buildings can potentially reveal possible consequences that are important for disaster mitigation and decision making. However, such a simulation involving all the buildings in a region can be computationally intensive. In this study, a computational framework using a network of distributed computers, each equipped with graphics processing units (GPUs), is proposed. The computational framework includes two types of structural fidelity models. For high-fidelity models, which are employed to analyze complex and/or important buildings, an efficient GPU-based linear equation solver is developed and incorporated in OpenSees, an open source computational platform commonly used for structural and earthquake engineering simulations of buildings and civil infrastructures. To handle the large number of computationally intensive high-fidelity structural models in a region, a dynamic load balancing strategy is designed to distribute the computational tasks among the available resources. For moderate-fidelity models, which are used to model regular building structures, a GPU-based tool is developed to accelerate the simulation. A static load balancing strategy is designed to distribute the computational tasks among the GPUs. To demonstrate the potential for a cost-effective and flexible computing paradigm for regional seismic simulation, the computational framework is applied to perform seismic simulation of a virtual city with 50 high-fidelity structural models and 100,000 moderate-fidelity building models.

Introduction

Cities are generally densely populated with many buildings and civil infrastructures. A strong earthquake occurring in a city can have a devastated consequence with many casualties and significant financial losses. For example, the 2011 Christchurch earthquake caused 185 deaths and a loss of US$ 11–15 billion, and significantly impacted the economy of New Zealand [1]. Regional seismic damage simulation can potentially provide valuable information that can facilitate decision making, enhance planning for disaster mitigation, and reduce human and economic losses.

Various models with different levels of fidelities have been proposed for regional seismic damage simulation of buildings [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]. These models can be divided into three levels: high-, moderate- and low-fidelity models:

• The structural models that involve detailed modeling of every beam, column and wall in a building are referred to as “high-fidelity models”, such as the finite element (FE) tall building models using fiber beam elements and multi-layer shell elements proposed by Lu et al. [12]. For buildings with important functions (e.g., hospitals and bridges) or with special structural arrangements (e.g., large span stadiums and stations, super-tall and irregular buildings), high-fidelity models are necessary to provide accurate and detailed seismic damage simulation that is essential for further seismic loss analyses (e.g. repair costs and downtime) [13], [14], [15].

• The structural models using single-degree-of-freedom (SDOF) model or other simple “equivalent” models are referred to as “low-fidelity models”. Examples of such models include the advanced engineering building modules (AEBM) proposed by the Federal Emergency Management Agency (FEMA) of the United States [16], [17], and the damage probability matrix method [2]. As previously discussed by Lu et al. [18], low-fidelity models, although computationally efficient, are not able to simulate many important damage features such as localized damages at the story levels.

• Moderate-fidelity structural models are simplified models that are sufficient to determine important structural damages such as detection of softened stories. One example is the multi-degree-of-freedom (MDOF) concentrated mass shear (MCS) model that can fully represent the nonlinear characteristics of soft-story failure by story levels of a building [18]. The MCS model requires only specification of five general characteristics of a building (i.e., structural type, construction year, area, building height, and number of stories) that can greatly simplify modeling of large number of regular buildings in a region.

In this study, we propose using high-fidelity models for the simulation of selected important and specialty buildings to evaluate in details the seismic performance of the buildings. On the other hand, moderate-fidelity models are employed to assess the damage levels of each of the thousands of regular buildings in a city.

Clearly, seismic simulation involving hundreds and thousands of buildings in a city is a computationally challenging problem. Yamashita et al. [6] employed supercomputer capability to perform regional seismic simulation with multi-fidelity models. Instead of supercomputers which are expensive to acquire and costly to maintain, distributed computing involving cluster of networked computers, each equipped with graphics processing units (GPUs), can be a cost effective alternative that is affordable even for engineering offices. GPU allows parallel computations to accelerate algorithmic calculations on a standalone computer. Furthermore, distributed computing can be employed to handle large number of buildings in a city by distributing the computational tasks to the networked computers with GPUs.

Because of its powerful parallel computing capability and low cost, GPU technology has been widely employed in many science and engineering fields, including biology, electromagnetism, geography, and others [19], [20], [21], [22], [23]. For seismic damage simulation of high-fidelity models, GPUs can be used to accelerate many of the matrix calculations in a finite element program, such as solutions of systems of equations and eigenvalue problems [24], [25]. For the seismic damage simulation of a large number of moderate-fidelity models, Lu et al. [18] used a standalone computer equipped with a GPU to perform time history analysis (THA) using the MCS models and obtained a speedup of 39 times over the same simulation utilizing a non-GPU computer of the same price.

Distributed computing is a flexible and easily reconfigurable platform that takes full advantage of networked computers by dynamically adjusting the computational resources according to the computational loads [26]. With adequate computational resources, distributed computing can be highly efficient to handle many large scale scientific problems [27], [28], [29], [30], [31], [32]. For instance, Wijerathne et al. [33] used a cluster of workstations to simulate the seismic damage of buildings in Tokyo. Many distributed computing platforms, such as BOINC [34], Hadoop [35] and HTCondor [36], are now available. This study employs HTCondor for implementation because HTCondor is open-source and supports GPUs for distributed computing [37], [38].

This study explores the use of both GPU computing and distributed computing for regional seismic damage simulation. A computational framework using a network of distributed computers, each equipped with a GPU, is proposed. The investigation includes developing distributed load balancing strategies as well as improving computational throughputs. For high-fidelity models, OpenSees (Open System for Earthquake Engineering Simulation) is employed [39]. Specifically, a linear equation solver that takes full advantage of the parallel computing capability of GPUs is incorporated in OpenSees to expedite the solution process. Furthermore, a greedy, dynamic load balancing strategy is designed for distributing the high-fidelity building models among the available resources. For moderate-fidelity models, a GPU-based implementation of the MCS models is employed in this study. A static load balancing strategy to handle the large number of regular building structures is proposed. To demonstrate the potential of a cost-effective and flexible computing paradigm for simulating seismic damage to buildings in a regional scale, the computational framework is applied to seismic simulation of a virtual city with 50 high-fidelity structural models and 100,000 moderate-fidelity building models.