Integration of graphical, physics-based, and machine learning methods for assessment of impact and recovery of the built environment from wind hazards
| dc.contributor.author | Pilkington, Stephanie F., author | |
| dc.contributor.author | Mahmoud, Hussam, advisor | |
| dc.contributor.author | Ellingwood, Bruce, committee member | |
| dc.contributor.author | van de Lindt, John, committee member | |
| dc.contributor.author | Zahran, Sammy, committee member | |
| dc.contributor.author | McAllister, Therese, committee member | |
| dc.contributor.author | Hamideh, Sara, committee member | |
| dc.date.accessioned | 2019-09-10T14:36:25Z | |
| dc.date.available | 2019-09-10T14:36:25Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The interaction between a natural hazard and a community has the potential to result in a natural disaster with substantial socio-economic losses. In order to minimize disaster impacts, researchers have been improving building codes and exploring further concepts of community resilience. Community resilience refers to a community's ability to absorb a hazard (minimize impacts) and "bounce back" afterwards (quick recovery time). Therefore, the two main components in modeling resilience are: the initial impact and subsequent recovery time. With respect to a community's building stock, this entails the building damage state sustained and how long it takes to repair and reoccupy that building. In modeling these concepts, probabilistic and physics-based methods have been the traditional approach. With advancements in artificial intelligence and machine learning, as well as data availability, it may be possible to model impact and recovery differently. Most current methods are highly constrained by their topic area, for example a damage state focuses on structural loading and resistance, while social vulnerability independently focus on certain social demographics. These models currently perform independently and are then aggregated together, but with the complex connectivity available through machine learning, structural and social characteristics may be combined simultaneously in one network model. The popularity of machine learning predictive modeling across multiple different applications has risen due to the benefit of modeling complex networks and perhaps identifying critical variables that were previously unknown, or the mechanism behind how these variables interacted within the predictive problem being modeled. The research presented herein outlines a method of using artificial neural networks to model building damage and recovery times. The incorporation of graph theory to analyze the resulting models also provides insight into the "black box" of artificial intelligence and the interaction of socio-technical parameters within the concept of community resilience. The subsequent neural network models are then verified through hindcasting the 2011 Joplin tornado for individual building damage and the time it took to repair and reoccupy each building. The results of this research show viability for using these methods to model damage, but more research work may be needed to model recovery at the same level of accuracy as damage. It is therefore recommended that artificial neural networks be primarily used for problems where the variables are well known but their interactions are not as easily understood or modeled. The graphical analysis also reveals an importance of social parameters across all points in the resilience process, while the structural components remain mostly important in determining the initial impact. Final importance factors are determined for each of the variables evaluated herein. It is suggested moving forward, that modeling approaches consider integrating how a community interacts with its infrastructure, since the human components are what make a natural hazard a disaster, and tracing artificial neural network connections may provide a starting point for such integration into current traditional modeling approaches. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Pilkington_colostate_0053A_15633.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/197404 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | building recovery | |
| dc.subject | graph theory | |
| dc.subject | wind damage | |
| dc.subject | community resilience | |
| dc.subject | artificial neural networks | |
| dc.subject | socio-technical | |
| dc.title | Integration of graphical, physics-based, and machine learning methods for assessment of impact and recovery of the built environment from wind hazards | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Civil and Environmental Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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