Matching Items (134)
Description
A simplified bilinear moment-curvature model are derived based on the moment-curvature response generated from a parameterized stress-strain response of strain softening and or strain-hardening material by Dr. Barzin Mobasher and Dr. Chote Soranakom. Closed form solutions are developed for deflection calculations of determinate beams subjected to usual loading patterns at any load stage. The solutions are based on a bilinear moment curvature response characterized by the flexural crack initiation and ultimate capacity based on a deflection hardening behavior. Closed form equations for deflection calculation are presented for simply supported beams under three point bending, four point bending, uniform load, concentrated moment at the middle, pure bending, and for cantilever beam under a point load at the end, a point load with an arbitrary distance from the fixed end, and uniform load. These expressions are derived for pre-cracked and post cracked regions. A parametric study is conducted to examine the effects of moment and curvature at the ultimate stage to moment and curvature at the first crack ratios on the deflection. The effectiveness of the simplified closed form solution is demonstrated by comparing the analytical load deflection response and the experimental results for three point and four point bending. The simplified bilinear moment-curvature model is modified by imposing the deflection softening behavior so that it can be widely implemented in the analysis of 2-D panels. The derivations of elastic solutions and yield line approach of 2-D panels are presented. Effectiveness of the proposed moment-curvature model with various types of panels is verified by comparing the simulated data with the experimental data of panel test.
ContributorsWang, Xinmeng (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, two problems are addressed in the verification and control of Cyber-Physical Systems (CPS):
1) Falsification: given a CPS, and a property of interest that the CPS must satisfy under all allowed operating conditions, does the CPS violate, i.e. falsify, the property?
2) Conformance testing: given a model of a CPS, and an implementation of that CPS on an embedded platform, how can we characterize the properties satisfied by the implementation, given the properties satisfied by the model?
Both problems arise in the context of Model-Based Design (MBD) of CPS: in MBD, the designers start from a set of formal requirements that the system-to-be-designed must satisfy.
A first model of the system is created.
Because it may not be possible to formally verify the CPS model against the requirements, falsification tries to verify whether the model satisfies the requirements by searching for behavior that violates them.
In the first part of this dissertation, I present improved methods for finding falsifying behaviors of CPS when properties are expressed in Metric Temporal Logic (MTL).
These methods leverage the notion of robust semantics of MTL formulae: if a falsifier exists, it is in the neighborhood of local minimizers of the robustness function.
The proposed algorithms compute descent directions of the robustness function in the space of initial conditions and input signals, and provably converge to local minima of the robustness function.
The initial model of the CPS is then iteratively refined by modeling previously ignored phenomena, adding more functionality, etc., with each refinement resulting in a new model.
Many of the refinements in the MBD process described above do not provide an a priori guaranteed relation between the successive models.
Thus, the second problem above arises: how to quantify the distance between two successive models M_n and M_{n+1}?
If M_n has been verified to satisfy the specification, can it be guaranteed that M_{n+1} also satisfies the same, or some closely related, specification?
This dissertation answers both questions for a general class of CPS, and properties expressed in MTL.
1) Falsification: given a CPS, and a property of interest that the CPS must satisfy under all allowed operating conditions, does the CPS violate, i.e. falsify, the property?
2) Conformance testing: given a model of a CPS, and an implementation of that CPS on an embedded platform, how can we characterize the properties satisfied by the implementation, given the properties satisfied by the model?
Both problems arise in the context of Model-Based Design (MBD) of CPS: in MBD, the designers start from a set of formal requirements that the system-to-be-designed must satisfy.
A first model of the system is created.
Because it may not be possible to formally verify the CPS model against the requirements, falsification tries to verify whether the model satisfies the requirements by searching for behavior that violates them.
In the first part of this dissertation, I present improved methods for finding falsifying behaviors of CPS when properties are expressed in Metric Temporal Logic (MTL).
These methods leverage the notion of robust semantics of MTL formulae: if a falsifier exists, it is in the neighborhood of local minimizers of the robustness function.
The proposed algorithms compute descent directions of the robustness function in the space of initial conditions and input signals, and provably converge to local minima of the robustness function.
The initial model of the CPS is then iteratively refined by modeling previously ignored phenomena, adding more functionality, etc., with each refinement resulting in a new model.
Many of the refinements in the MBD process described above do not provide an a priori guaranteed relation between the successive models.
Thus, the second problem above arises: how to quantify the distance between two successive models M_n and M_{n+1}?
If M_n has been verified to satisfy the specification, can it be guaranteed that M_{n+1} also satisfies the same, or some closely related, specification?
This dissertation answers both questions for a general class of CPS, and properties expressed in MTL.
ContributorsAbbas, Houssam Y (Author) / Fainekos, Georgios (Thesis advisor) / Duman, Tolga (Thesis advisor) / Mittelmann, Hans (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015
Description
Unidirectional glass fiber reinforced polymer (GFRP) is tested at four initial strain rates (25, 50, 100 and 200 s-1) and six temperatures (−25, 0, 25, 50, 75 and 100 °C) on a servo-hydraulic high-rate testing system to investigate any possible effects on their mechanical properties and failure patterns. Meanwhile, for the sake of illuminating strain rate and temperature effect mechanisms, glass yarn samples were complementally tested at four different strain rates (40, 80, 120 and 160 s-1) and varying temperatures (25, 50, 75 and 100 °C) utilizing an Instron drop-weight impact system. In addition, quasi-static properties of GFRP and glass yarn are supplemented as references. The stress–strain responses at varying strain rates and elevated temperatures are discussed. A Weibull statistics model is used to quantify the degree of variability in tensile strength and to obtain Weibull parameters for engineering applications.
ContributorsOu, Yunfu (Author) / Zhu, Deju (Author) / Zhang, Huaian (Author) / Huang, Liang (Author) / Yao, Yiming (Author) / Li, Gaosheng (Author) / Mobasher, Barzin (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2016-05-19
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05