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You searched for +publisher:"Purdue University" +contributor:("Julio A. Ramirez"). Showing records 1 – 2 of 2 total matches.

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Purdue University

1. Carrillo, Mayren Yelitza Mata. Evaluation of methods for estimating prestress losses in high-strength structural concrete.

Degree: MSCE, Civil Engineering, 2014, Purdue University

The purpose of this study was to evaluate the applicability of several current approaches used to estimate losses in prestressed concrete members with compressive strengths greater than 15ksi. The scope of the study focused on time-dependent losses for normal weight concrete bonded applications. The approaches evaluated were the PCI Design Handbook (2010) method, the AASHTO Specifications (2012) refined method, the PCI Bridge Design Manual (2003) time-dependent analysis using both the AASHTO (2012) and the PCI-BDM (2003) creep and shrinkage models, and a time-step method developed by Swartz (2010). The methods were compared to existing data on prestress losses from twenty-two specimens with compressive strengths from 11ksi to 18ksi. However, a paucity of data existed for strengths higher than 15ksi, with only seven specimens available. Therefore, insufficient data was available to justify a change to the current limit. Based on the comparison of all approaches evaluated, the PCI-BDM (2003) time-dependent method using the PCI-BDM (2003) creep and shrinkage models was shown to give conservative estimates close to the measured losses from the available specimen data. If a simpler analysis is desired, the AASHTO (2012) refined method could be applied. Although, caution is recommended when using this method, since the analysis conducted in this study showed that it could result in an underestimation of the losses within the range of existing data. Considering the scatter in the available data, it is recommended that more tests be carried out in order to properly evaluate extension of current approaches to design concrete strengths greater than the current limit of 15ksi. Guidance is given in this thesis on the key design parameters that should be considered in such experimental evaluation. Advisors/Committee Members: Julio A. Ramirez, Julio A. Ramirez, Ghadir Haikal, Michael E. Kreger.

Subjects/Keywords: Civil Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Carrillo, M. Y. M. (2014). Evaluation of methods for estimating prestress losses in high-strength structural concrete. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1029

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Carrillo, Mayren Yelitza Mata. “Evaluation of methods for estimating prestress losses in high-strength structural concrete.” 2014. Thesis, Purdue University. Accessed February 17, 2020. https://docs.lib.purdue.edu/open_access_theses/1029.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Carrillo, Mayren Yelitza Mata. “Evaluation of methods for estimating prestress losses in high-strength structural concrete.” 2014. Web. 17 Feb 2020.

Vancouver:

Carrillo MYM. Evaluation of methods for estimating prestress losses in high-strength structural concrete. [Internet] [Thesis]. Purdue University; 2014. [cited 2020 Feb 17]. Available from: https://docs.lib.purdue.edu/open_access_theses/1029.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Carrillo MYM. Evaluation of methods for estimating prestress losses in high-strength structural concrete. [Thesis]. Purdue University; 2014. Available from: https://docs.lib.purdue.edu/open_access_theses/1029

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Purdue University

2. Yeum, Chul Min. Computer vision-based structural assessment exploiting large volumes of images.

Degree: PhD, Civil Engineering, 2016, Purdue University

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual information. Recent advances in computer vision to explore new sensors, sensing platforms and high-performance computing have shed light on the potential for vision-based visual assessment in civil engineering structures. The use of low-cost, high-resolution visual sensors in conjunction with mobile and aerial platforms can overcome spatial and temporal limitations typically associated with other forms of sensing in civil structures. Also, GPU-accelerated and parallel computing offer unprecedented speed and performance, accelerating processing the collected visual data. However, despite the enormous endeavor in past research to implement such technologies, there are still many practical challenges to overcome to successfully apply these techniques in real world situations. A major challenge lies in dealing with a large volume of unordered and complex visual data, collected under uncontrolled circumstance (e.g. lighting, cluttered region, and variations in environmental conditions), while just a tiny fraction of them are useful for conducting actual assessment. Such difficulty induces an undesirable high rate of false-positive and false-negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome the inherent challenges in using such images for visual assessment, high-level computer vision algorithms must be integrated with relevant prior knowledge and guidance, thus aiming to have similar performance with those of humans conducting visual assessment. Moreover, the techniques must be developed and validated in the realistic context of a large volume of real-world images, which is likely contain numerous practical challenges. In this dissertation, the novel use of computer vision algorithms is explored to address two promising applications of vision-based visual assessment in civil engineering: visual inspection, and visual data analysis for post-disaster evaluation. For both applications, powerful techniques are developed here to enable reliable and efficient visual assessment for civil structures and demonstrate them using a large volume of real-world images collected from actual structures. State-of-art computer vision techniques, such as structure-from-motion and convolutional neural network techniques, facilitate these tasks. The core techniques derived from this study are scalable and expandable to many other applications in vision-based visual assessment, and will serve to close the existing gaps between past research efforts and real-world implementations. Advisors/Committee Members: Shirley J. Dyke, Shirley J. Dyke, Bedrich Benes, Zygmunt Pizlo, Santiago Pujol, Julio A. Ramirez, Juan Wachs.

Subjects/Keywords: Applied sciences; Computer vision; Convolutional neural network; Multi-view geometry; Structure-from-motion; Visual assessment; Civil Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yeum, C. M. (2016). Computer vision-based structural assessment exploiting large volumes of images. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1036

Chicago Manual of Style (16th Edition):

Yeum, Chul Min. “Computer vision-based structural assessment exploiting large volumes of images.” 2016. Doctoral Dissertation, Purdue University. Accessed February 17, 2020. https://docs.lib.purdue.edu/open_access_dissertations/1036.

MLA Handbook (7th Edition):

Yeum, Chul Min. “Computer vision-based structural assessment exploiting large volumes of images.” 2016. Web. 17 Feb 2020.

Vancouver:

Yeum CM. Computer vision-based structural assessment exploiting large volumes of images. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2020 Feb 17]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1036.

Council of Science Editors:

Yeum CM. Computer vision-based structural assessment exploiting large volumes of images. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1036

.