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Automated structural damage diagnosis after earthquakes is important for improving efficiency of disaster response and city rehabilitation. In conventional data-driven frameworks which use machine learning or statistical models, structural damage diagnosis models are often constructed using supervised learning. Supervised learning requires historical structural response data and corresponding damage states (i.e., labels) for each building to learn the building-specific damage diagnosis model. However, in post-earthquake scenarios, historical data with labels are often not available for many buildings in the affected area. This makes it difficult to construct a damage diagnosis model. Further, directly using the historical data from other buildings to construct a damage diagnosis model for the target building would lead to inaccurate results. This is because each building has unique physical properties and thus unique data distribution. To this end, we introduce a new framework, Physics-Informed Multi-source Domain Adversarial Networks (PhyMDAN), to transfer the model learned from other buildings to diagnose structural damage states in the target building without any labels. This framework is based on an adversarial domain adaptation approach that extracts domain-invariant feature representations of data from different buildings. The feature extraction function is trained in an adversarial way, which ensures that extracted feature distributions are robust to variations of structural properties. The feature extraction function is simultaneously jointly trained with damage prediction function to ensure extracted features being predictive for structural damage states. With extracted domain-invariant feature representations, data distributions become consistent across different buildings. We evaluate our framework on both numerical simulation and field data collected from multiple building structures. The results show up to 90.13% damage detection accuracy and 84.47% damage quantification accuracy on simulation data, and up to 100% damage detection accuracy and 69.93% 5-class damage quantification accuracy when transferring from numerical simulation data to real-world experimental data, which outperforms the state-of-the-art benchmark methods.
Susu Xu; Hae Young Noh. PhyMDAN: Physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning. Mechanical Systems and Signal Processing 2020, 151, 107374 .
AMA StyleSusu Xu, Hae Young Noh. PhyMDAN: Physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning. Mechanical Systems and Signal Processing. 2020; 151 ():107374.
Chicago/Turabian StyleSusu Xu; Hae Young Noh. 2020. "PhyMDAN: Physics-informed knowledge transfer between buildings for seismic damage diagnosis through adversarial learning." Mechanical Systems and Signal Processing 151, no. : 107374.
Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).
Anand Krishnan Prakash; Susu Xu; Ram Rajagopal; Hae Young Noh. Robust Building Energy Load Forecasting Using Physically-Based Kernel Models. Energies 2018, 11, 862 .
AMA StyleAnand Krishnan Prakash, Susu Xu, Ram Rajagopal, Hae Young Noh. Robust Building Energy Load Forecasting Using Physically-Based Kernel Models. Energies. 2018; 11 (4):862.
Chicago/Turabian StyleAnand Krishnan Prakash; Susu Xu; Ram Rajagopal; Hae Young Noh. 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models." Energies 11, no. 4: 862.
This paper introduces an indirect train traffic monitoring method to detect and infer real-time train events based on the vibration response of a nearby building. Monitoring and characterizing traffic events is important for cities to improve the efficiency of transportation systems (e.g., train passing, heavy trucks, traffic). Most prior work falls into two categories: 1) methods that require intensive labor to manually record events or 2) systems that require deployment of dedicated sensors. These approaches are difficult and costly to execute and maintain. In addition, most prior work uses dedicated sensors designed for a single purpose, resulting in deployment of multiple sensor systems. This further increases costs. Meanwhile, with the increasing demands of structural health monitoring, many vibration sensors are being deployed in commercial buildings. Traffic events create ground vibration that propagates to nearby building structures inducing noisy vibration responses. We present an information theoretic method for train event monitoring using commonly existing vibration sensors deployed for building health monitoring. The key idea is to represent the wave propagation in a building induced by train traffic as information conveyed in noisy measurement signals. Our technique first uses wavelet analysis to detect train events. Then, by analyzing information exchange patterns of building vibration signals, we infer the category of the events (i.e., southbound or northbound train). Our algorithm is evaluated with an 11-story building where trains pass by frequently. The results show that the method can robustly achieve a train event detection accuracy of up to a 93% true positive rate and a 80% true negative rate. For direction categorization, compared with the traditional signal processing method, our information-theoretic approach reduces categorization error from 32.1% to 12.1%, which is a 2.5X improvement.
Susu Xu; Lin Zhang; Pei Zhang; Hae Young Noh. An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration. Frontiers in Built Environment 2017, 3, 1 .
AMA StyleSusu Xu, Lin Zhang, Pei Zhang, Hae Young Noh. An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration. Frontiers in Built Environment. 2017; 3 ():1.
Chicago/Turabian StyleSusu Xu; Lin Zhang; Pei Zhang; Hae Young Noh. 2017. "An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration." Frontiers in Built Environment 3, no. : 1.