7.1 Summary
Rapid assessment of building condition following seismic events, either low-probability extreme events or more frequent occurrences of lower intensity events, is of critical importance for densely populated urban regions. The main objective of this dissertation is to develop a data- driven building seismic response prediction framework to interpolate damage-related Engineering Demand Parameters (EDP) for a cluster of buildings using a limited number of recordings from instrumented buildings. The proposed framework can be used for rapid damage identification of buildings subjected to a seismic event and rapid seismic risk estimation for an individual building or a cluster of buildings at a regional scale. The proposed data-driven models are demonstrated and validated through 1) simulated seismic responses of various buildings subjected to numerous ground motions using Perform3D [43] and OpenSees [42]; and 2) recorded building responses from 188 buildings from 24 historical earthquakes in California.
An overview of Machine Learning (ML) methods and their applications within structural engineering problems are presented in Chapter 2. ML applications in structural engineering are examined for collecting data, constructing models, and enhancing computation tools, followed by a motivations for expanding the range of applications for ML. Finally, a critical assessment of future directions for application of ML is presented, including discussions related to data source, model interpretation, and model extrapolation.
Chapter 3 covers an inter-building interpolation model relying on the spatial and structural correlation of responses in co-regionally located buildings subjected to a seismic event. A dataset of response demands for a portfolio of reinforced concrete moment frame buildings is generated by performing Non-linear Response History Analyses (NRHA) on structural models using ground
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motions recorded from historical scenario earthquakes. The dataset is used to characterize the correlation between seismic demands across different buildings. Semi-variograms are used to model spatial and structural correlation and then incorporated into a Kriging algorithm, which forms the basis of the interpolation models. Chapter 4 further expands the Kriging interpolation model into a full-profile seismic response demands reconstruction model across multiple tall buildings using kernel-based ML methods. NRHA are used to generate a dataset of Peak Floor Accelerations (PFA) and Peak Story Drift Ratios (PSDR) for a portfolio of tall buildings, using spatially explicit ground motions from the Northridge earthquake. Structural dissimilarities are incorporated by including a range of building heights and differences in the type and combination of lateral force resisting systems. Using measurements from limited locations within a subset of buildings, the full-profile response demands for all buildings in a portfolio are reconstructed. A rigorous evaluation procedure is used to demonstrate the ability of the kernel-based methods to accurately capture the highly nonlinear response demand patterns within and across buildings.
The previous two methods are event-based; in other words, the model used for reconstruction of seismic demands is limited to a single earthquake, i.e., event characteristics are not considered. Chapter 5 introduces a generalized EDP reconstruction model based on mix-effect model that incorporates source, path, and site terms, which is demonstrated by using recorded seismic building response data from 1984 to 2016 from 188 buildings subjected to 24 earthquakes in California. Two versions of the model are proposed, one is calibrated from the raw data and the other is adopted from Ground Motion Prediction Equations for both PSDR and PFA. The total residuals from this model are evaluated by further decoupling within-event residuals and site residuals. A Kriging interpolation is applied on the remaining residuals to evaluate demand reconstruction accuracy. Chapter 6 further improves the decoupling procedure of within-event
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residual using a Bayesian hierarchical model that properly addresses the issue of insufficient data within certain events. A Markov Chain Monte Carlo technique, Gibbs sampler, is applied to retrieve model parameters and convergence of the chain is rigorously validated. Within-event residuals retrieved using two different approaches, the sample mean from Chapter 5 and the Bayesian hierarchical model from Chapter 6, are compared with each other.
7.2 Key Findings
Fidelity of data-driven models primarily depends on data quality, model design logic, and model evaluation procedures. Development of data-driven models should be conducted based on the above three areas. Two categories of data used in this study, simulated and recorded seismic building responses, respectively. The simulated data are applied to evaluate the two within-event building seismic response prediction models, the kriging interpolation model and the kernel-based ML model. The kriging model quantifies spatial similarity through semi-variograms and constructs a series of semi-variograms for structural dissimilarity. The kernel-based ML model applies kernel functions as a generalized means to incorporate dissimilarities. Kernel is found to be a more representative and generalized dissimilarity measure to account for features from different perspectives, in our case, spatial, structural and within-building height.
Model performance is found to be mainly driven by how representative the data are and how efficient the model is in fitting the data patterns. For example, it is observed that the PFA data show relatively more regulated patterns across different buildings, as PFAs are found to have much lower within-building dispersion and variation at higher demand levels compared to PSDRs. However, it does not necessarily suggest a better prediction performance as observed in several model performance evaluation cases.
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The performance of the generalized cross-building EDP reconstruction model has been significantly improved by adopting a GMPE estimation which is extensively calibrated according to earthquake mechanisms. In addition, the Bayesian hierarchical model for within-event residuals is found to be more reliable compared to sample means due to its hierarchical structure that simulates the earthquake mechanism and distributes uncertainties based on physical scenarios.
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