A Probabilistic Decision Support System for a Performance-Based Design of Infrastructures
| dc.contributor.author | Shahtaheri, Yasaman | en |
| dc.contributor.committeechair | de la Garza, Jesus M. | en |
| dc.contributor.committeechair | Flint, Madeleine Marie | en |
| dc.contributor.committeemember | Rodriguez-Marek, Adrian | en |
| dc.contributor.committeemember | Wernz, Christian | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2020-02-12T07:00:30Z | en |
| dc.date.available | 2020-02-12T07:00:30Z | en |
| dc.date.issued | 2018-08-20 | en |
| dc.description.abstract | Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the epistemic uncertainty associated with the lack of knowledge of decision maker utilities. Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment, they do not provide a systematic framework for including aleatory and epistemic uncertainties, and decision maker utilities in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology (i.e., utility function/indifference curve) for assessing decision maker utility functions with respect to a range of alternative design configurations. Module two utilizes the First Order Reliability Method (FORM) in a systems reliability approach for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker defined utility functions (indifference curves), and provides a meaningful feedback loop for improving the reliability of the alternative design configurations. Module three provides a systematic framework to incorporate both aleatory and epistemic uncertainties in the decision analysis methodology (i.e., uncertain utility functions and group decision making). The multi-criteria, probabilistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations and four decision maker utility functions under aleatory and epistemic uncertainties are utilized. The results of the decision analysis methodology revealed that the high-performing design configurations with an initial cost of up to $3.2M (in a cost range between $1.7M and $3.2M), a building damage and business interruption cost as low as $303K (in a cost range between $303K and $6.2M), a casualty cost as low as $43K (in a cost range between $43K and $1.2M), and a CO2 emission as low as $146K (in a cost range between $133K to $150K) can be identified by having a higher probability (i.e., up to 80%) of meeting the decision makers' preferences. The modular, holistic, decision analysis framework allows decision makers to make more informed performance-based design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process. | en |
| dc.description.abstractgeneral | Infrastructures, including buildings, roads, and bridges, are the most fundamental facilities and systems serving the society. Because infrastructures exist in economic, social, and environmental contexts, the design, construction, operations, and maintenance phases of such fundamental facilities should maximize value and usability for the designers, occupants, and the society. Identifying infrastructure configurations that maximize value and usability is challenged by two sources of uncertainty: 1) the nature of the built environment is variable (i.e., whether or not a natural hazard will occur during the infrastructure lifetime, or how costs might change over time); and 2) there is lack of knowledge of decision maker preferences and values (e.g., design cost versus social impact tradeoffs). Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment (e.g., natural hazard events), they do not provide a systematic framework for including the uncertainties associated with the decision analysis process (e.g., lack of knowledge about decision maker preferences), and decision maker requirements in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology for assessing decision maker values with respect to a range of alternative design configurations. Module two utilizes an algorithm for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker requirements, and provides a meaningful feedback loop for understanding the decision analysis results (i.e., improving the value and usability of the alternative design configurations). Module three provides a systematic framework to incorporate both the random uncertainty associated with the built environment and the knowledge uncertainty associated with lack of knowledge of decision maker preferences, and tests the reliability of the decision analysis results under random and knowledge uncertainties (i.e., uncertain decision maker preferences and group decision making). The holistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations, four decision makers, and random and knowledge sources of uncertainty are considered in the decision analysis methodology. Results indicate that the modular, holistic, decision analysis framework allows decision makers to make more informed design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process. | en |
| dc.description.degree | PHD | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:16625 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/96804 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Infrastructure | en |
| dc.subject | Performance-Based | en |
| dc.subject | Design Strategies | en |
| dc.subject | Decision Analysis | en |
| dc.subject | Multi-Criteria | en |
| dc.subject | Multi-Objective | en |
| dc.subject | Optimization | en |
| dc.subject | Aleatory Uncertainty | en |
| dc.subject | Epistemic Uncertainty | en |
| dc.subject | System Reliability | en |
| dc.subject | Utility Function | en |
| dc.subject | First Order Reliability Method | en |
| dc.title | A Probabilistic Decision Support System for a Performance-Based Design of Infrastructures | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | PHD | en |
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