Structural Health Monitoring Framework using Acceleration Data and Machine Learning Techniques

Structural health monitoring (SHM) is becoming more and more important as the civil infrastructure systems in the US are aging. Since replacing all these structures are not feasible, SHM is necessary to monitor the structural integrity and assess the structural deterioration for safe and continuous operation of the structural systems. The monitoring of structures becomes more critical in the event of an earthquake as the lack of knowledge immediately following an earthquake about the structural condition may result in population displacement, business disruption, and an extended recovery time. In this study, a SHM framework is developed for rapid post-earthquake damage assessment. A damage feature called the cumulative absolute velocity (CAV) is used and machine learning techniques are employed. The applicability of the framework is tested using data from laboratory experiments, real structures, as well as numerical analysis./nAlthough there are numerous damage features, only a handful of them can be applied to strong motion data. A wave-based feature that is simple in nature, scalable, easily computed with limited computational effort, and contains information from the whole signal is yet to be developed. Therefore, this study evaluates the application of the CAV as a local damage indicator. The response of a bridge column test specimen instrumented with 18 accelerometers and subjected to increasing six levels of shaking is investigated to understand the relationship between CAV and damage. This damage feature is subsequently evaluated for two instrumented buildings damaged during earthquakes in California: Van Nuys hotel and Imperial County Services (ICS) building. The results show that for the 7-story Van Nuys hotel, the CAV can correctly locate the damage that occurred during the 1994 Northridge earthquake. For the ICS building, the CAV is also able to correctly identify the location of damage. Thus, the presented CAV feature enables identifying the onset and location of damage, confirming the hypothesis that CAV is correlated with structural damage./nTo automate and expedite the process of damage assessment, a methodology is introduced using low dimensional, CAV-based feature within a machine learning (ML) algorithm. The appropriate features and ML algorithms are identified by analyzing a single degree of freedom (SDOF) OpenSEES model. A comparative analysis of four ML algorithms is performed using three CAV-based features to determine the ideal feature and ML tool. The analysis showed that when absolute and relative (with respect to linear response) CAV features are used together, the highest accuracy is obtained. Moreover, ordinal logistic regression (OLR) is shown to be a suitable ML approach. Subsequently, OLR is applied with the identified features to classify damage of two multi-degree of freedom (MDOF) systems representing five-story buildings. One of the systems has uniform story shear capacity (MDOF-US) and the other has non-uniform (MDOF-NS) story shear capacity. Results show that the proposed method assess severity and location of damage with high accuracy (greater than 90\%) when damage information is available through an experimental study or an analytical model. However, such models are typically not available for existing structures in most cases. Therefore, it may not be considered feasible to develop one just for the sole purpose of SHM due to cost, time, or resource limitations. Accordingly, an SHM framework called the human-machine collaboration (H-MC) is proposed for existing structures with limited data which attempts to use the advantages provided by the ML and the knowledge of a human expert. In this way, domain expertise and data science are efficiently combined to solve the SHM problem. Subsequently, the framework is applied to detect damage in selected fifteen California Strong Motion Instrumentation Program (CSMIP) instrumented buildings. The results show that the H-MC algorithm correctly labeled the undamaged and damaged cases. Moreover, it eliminated false positive detection, which is a concern in decision making based on conventional SHM. This framework opens up the opportunity to automate the process of damage detection and assess and notify the risk associated with each structure in near-real time after an earthquake./nA probabilistic decision analysis method PBE-MAUT is also presented in this study. It integrates the powerful and efficient MAUT with the PBE methodology. It provides the decision maker with a robust quantitative tool to consider multiple criteria with different measuring units to facilitate the decision making process. The PBE-MAUT method is applied to compare different SHM systems considering cost and accuracy. Results show that the CAV-based method with all floors instrumented is the best option for a risk-neutral decision maker even though the option has the highest cost among all the options. The findings of this decision analysis, in turn, highlight the usefulness of instrumenting buildings and the associated effectiveness of the proposed SHM framework.

Advisor: Mosalam, Khalid M