Doctor of Philosophy, The Ohio State University, 2022, Civil Engineering

There are three main research conducted in this paper, including using deep learning methods with vision-based technologies on Structural Damage Detection (SDD), Structural Health Monitoring (SHM) and progressive collapse study. During the learning and improvement process, many goals of automation in SDD and SHM have been achieved, although there will be a large room for further improvement and development on these studies. In progressive collapse study, remote sensing technologies and data fusion are applied on a field experiment of a real building at the Central Campus of the Ohio State University. The major contributions of this paper are shown as follows: A few comprehensive experimental studies for automated SDD in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual Network (ResNet) is utilized to identify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, material types, etc. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipeline, cascaded networks, for categorizing and locating structural damage. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network. In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes. One of the proposed networks can achieve an accuracy above $67.6\%$ for all tested images at various scales and resolutions, and shows its robustness for these human-free detection tasks. Studies are conducted with a pipeline to automatically track and measure displacements and vibrations of structures or structural components in laboratory and field experiments. This novel framework (open full item for complete abstract)

Committee: Halil Sezen Dr. (Advisor); Farhang Pourboghrat Dr. (Committee Member); Rongjun Qin Dr. (Committee Member); Alper Yilmaz Dr. (Advisor) Subjects: Civil Engineering; Computer Science; Mechanics

Doctor of Philosophy, The Ohio State University, 2020, Civil Engineering

Vibration-based structural health monitoring (SHM) has become increasingly popular in recent years as a general and global method to detect possible damage scenarios. With the increase in the number of infrastructures that are in service beyond their initial design service age, more and more owners are relying on SHM to evaluate the integrity of their structures. As a result, SHM approaches that are applicable to a variety of structures with minimal service interruption and lower cost are of high importance. There are many research on SHM processes using a network of sensors placed on over a target structure. Although these approaches may result in more accurate results due to redundancy of the system, they are mostly cost prohibitive for currently in-service structures and are suitable for newly constructed projects with embedded sensors. This dissertation presents a feature-based SHM process using a new signal processing and feature extraction methodology and presents its application on a real-life vibration monitoring project completed of an energized substation structure. The new signal processing and feature extraction methodology uses specific filtering and optimization schemes which improved the performance in extracting features specifically when using a contaminated response signal. Next, the extracted features are used in a structural model updating to identify and localize the damage through an optimization process. Finally, a vortex-induced vibration analysis process is presented and applied to the real-life monitored structure. Currently there are no power utility industry standard methodology for the analysis and design of structures against wind-induced vibrations. The current codes or standards of practice recommend using damping devices such as chain dampers or strakes to mitigate the vibrations, when they are observed. This approach may not be feasible due to the energized in-service structures. In addition, modifications to the installed structure (open full item for complete abstract)

Committee: Abdollah Shafieezadeh (Advisor); Jieun Hur (Committee Member); Halil Sezen (Committee Member) Subjects: Engineering

MS, University of Cincinnati, 2008, Engineering : Mechanical Engineering

Vibration based damage detection of engineering structures has become an important and difficult issue for the last couple of decades. Research in vibration based structural damage detection has been rapidly expanding from traditional modal parameter estimation based techniques to modern feature based, online monitoring techniques. However, there is still a need for a universal structural damage detection method that does not depend on modal parameter estimation, finite element model or specific structural type. This research outlines and validates a Unified Matrix Polynomial Approach (UMPA) and subspace analysis based structural damage detection method. UMPA presents a theoretical basis and a fundamental mathematical framework for experimental modal parameter estimation algorithms while Singular Value Decomposition (SVD) based subspace analysis provides an mechanism to extract and compare the characteristic features from this mathematical framework to detect structural damage. Simulations were performed on an analytical 15 Degree of Freedom (DOF) mass-spring-damper system and a lightly damped circular plate finite element model to validate and assess the proposed structural damage detection method. The results show that the proposed method successfully identifies structural damage under all test conditions. The proposed method has a significant resistance to measurement uncertainty, has a good consistency with the severity of the damage and is applicable to various structural damage locations.

Committee: Randall J. Allemang PhD (Committee Chair); Teik C. Lim PhD (Committee Member); Allyn W. Phillips PhD (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2006, Engineering : Mechanical Engineering

The United States economy is heavily dependent upon a vast network of pipeline systems to transport and distribute the nation's energy resources. As this network of pipelines continues to age, monitoring and maintaining its structural integrity remains essential to the nation's energy interests. Numerous pipeline accidents over the past several years have resulted in hundreds of fatalities and billions of dollars in property damages. These accidents show that the current monitoring methods are not sufficient and leave a considerable margin for improvement. To avoid such catastrophes, more thorough methods are needed. As a solution, the research of this thesis proposes a structural health monitoring (SHM) system for pipeline networks. By implementing a SHM system with pipelines, their structural integrity can be continuously monitored, reducing the overall risks and costs associated with current methods. The proposed SHM system relies upon the deployment of macro fiber composite (MFC) patches for the sensor array. Because MFC patches are flexible and resilient, they can be permanently mounted to the curved surface of a pipeline's main body. From this location, the MFC patches are used to monitor the structural integrity of the entire pipeline. Two damage detection techniques, guided wave and impedance methods, were implemented as part of the proposed SHM system. However, both techniques utilize the same MFC patches. This dual use of the MFC patches enables the proposed SHM system to require only a single sensor array. The presented Lamb wave methods demonstrated the ability to correctly identify and locate the presence of damage in the main body of the pipeline system, including simulated cracks and actual corrosion damage. The presented impedance methods demonstrated the ability to correctly identify and locate the presence of damage in the flanged joints of the pipeline system, including the loosening of bolts on the flanges. In addition to damage to the actual pip (open full item for complete abstract)

Committee: Dr. Randall Allemang (Advisor) Subjects: Engineering, Mechanical

MS, University of Cincinnati, 2003, Engineering : Mechanical Engineering

Detecting damage in structures that are in service and operating is difficult using conventional non-destructive evaluation techniques. This thesis examines the use of acoustic emission and resulting waves in the structure to determine damage in the structure. In order to detect and measure the waves generated continuous sensors are used. Continuous sensors contain multiple interconnected sensor nodes that form an array of sensors covering the whole structure. A new concept of active fiber composite sensor is added to the continuous sensor. The use of active fiber sensor brings the possibility of unidirectional sensing in continuous sensor. The advantage of this passive health monitoring approach is that the sensors are highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the thesis, the continuous sensor is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected using continuous sensors. The simulation results show the possibility of unidirectional sensing and give some insight into the sensor response. Based on the simulation results the unidirectional sensor are constructed and tested. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Different designs and sensor configurations are examined and advantages are discussed. The sensor design proposed is manufactured in Smart Structures and Bio-Nanotechnology Laboratory. Step by step manufacturing of the active fiber composite sensors is also discussed in the thesis. The continuous sensors constructed in the lab are evaluated in a realistic test to show their ability to d (open full item for complete abstract)

Committee: Dr. Mark J. Schulz (Advisor) Subjects: Engineering, Mechanical

Doctor of Philosophy, The Ohio State University, 2005, Civil Engineering

A multi-paradigm nonparametric model, dynamic fuzzy wavelet neural network (WNN) model, is developed for structural system identification of three dimensional highrise buildings. The model integrates chaos theory (nonlinear dynamics theory), a signal processing method (wavelets), and two complementary soft computing methods (fuzzy logic and neural network). An adaptive Levenberg-Marquardt-least-squares learning algorithm is developed for adjusting parameters of the dynamic fuzzy WNN model. The methodology is applied to one five-story test frame and two highrise moment-resisting building structures. Results demonstrate that the methodology incorporates the imprecision existing in the sensor data effectively and balances the global and local influences of the training data. It therefore provides more accurate system identifications and nonlinear approximation with a fast training convergence. A nonparametric system identification-based model is developed for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy WNN model. The model does not require complete measurements of the dynamic responses of the whole structure. A damage evaluation method is proposed based on a power density spectrum method. The multiple signal classification method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using experimental data obtained for a 38-story concrete test model. It is demonstrated that the WNN model together with the pseudospectrum method is effective for damage detection of highrise buildings based on a small amount of sensed data. A nonlinear control model is developed for active control of highrise three dimensional building structures including geometrical and material nonlinearities, coupling action between lateral and torsional motions, and actuator dynamics. A dynamic fuzzy wavelet neuroemulator is developed for predicting the structural response in futur (open full item for complete abstract)

Committee: Hojjat Adeli (Advisor) Subjects: Engineering, Civil