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Numerical Simulation and Seismic Fragility and Risk Analysis of Wharves in Liquefiable Soils

Earthquakes pose a significant threat to many large U.S. seaports which serve as critical gateways for national and international trade. Considering the fact that disruption in port activities may lead to significant direct and indirect losses, seismic performance evaluation and retrofit of ports is quite essential. Current engineering practice for seismic risk reduction for port facilities is typically based on design or retrofit criteria for individual components expressed in terms of arbitrary levels of force and/or displacement. Seismic risk analysis on the other hand provides a framework through which both economic issues and system performance can be taken into account and the performance of the port can be seen as a whole. The aim of this research is developing various elements involved in seismic risk analysis of pile supported wharves. Following tasks are accomplished throughout this research.

  • Three-dimensional dynamic response of soil-foundation-wharf systems during seismic events is investigated using advanced numerical models including novel dynamic macroelements for nonlinear soil-pile interactions in liquefiable soils, force-based beam-column elements for piles, and experimentally calibrated nonlinear joint models for pile-deck connections.
  • Various critical behaviors of wharves are investigated such as pile and pile-deck connection failure mechanisms, permanent deformations of potentially liquefiable soil embankments, and effects of far-field and impulsive near-field ground motions on the torsional response of the wharves.
  • The specific effects of wharf-crane interactions on seismic behavior of wharves and cranes are investigated by incorporating an enhanced nonlinear sliding/uplift capable model of a jumbo container crane into the nonlinear wharf-foundation system model. The main conclusion of this study is that in contrary to the assumption in former studies, the contribution of the wharf-crane interaction on the total seismic response of the wharf can be significant.
  • A general probabilistic framework for correlated repair cost and downtime estimation of geo-structures exposed to seismic hazards is developed. The formulation of the repair cost and downtime accounts for the uncertainties associated with damage states and the reduction in the repair requirements as the number of damaged components in the given damage state increases. 

Risk Management of Deteriorating Power Distribution Assets against Hurricanes

Distribution of electricity in the United States relies on overhead lines supported by wood poles. This distribution system suffers damage in high wind events resulting in interruption in electrical power to businesses and residencies. The wood poles suffer from the combined effect of aging and increasing stress from wind hazards leading to an unreliable and potentially unsafe distribution infrastructure. Over 100 million distribution poles, which are part of the critical energy delivery infrastructure, suffer immensely during wind storms. Current methods for predicting the distribution of times-to-failure of a general set of power system components do not address simultaneous effects of aging and hazards with a holistic view. Research on age-at-failure of poles has been sparse, incomplete, and inconsistent. This results in increased uncertainties in predictions of pole lifetimes, thus obstructing informed and accurate decision-making on effective pole management strategies.

The project proposes the development of stochastic Finite Element models for distribution poles integrated with time-dependent decay models to simulate the current and future state of the response of distribution poles to various climate conditions. Epistemic uncertainties will be reduced and various sources of aleatoric uncertainties in capacity and demand modeling will be captured. A novel strategy utilizing n-dimensional fragility surfaces integrated with regional hurricane hazard models will be merged to estimate failure probabilities, develop predictive models for the time-to-failure of distribution poles, and quantify the confidence levels in the predictions. A priority-based diagnostics and risk-informed maintenance framework will be developed on the basis of stochastic optimization for distribution networks that takes into account both the geographic exposure of the physical components to stressors and their importance for the reliability of the networks. The success afforded by this approach does not guarantee failure avoidance, but points to the regions in networks where use of well-maintained infrastructure may lower the risk of failures during storms and also provides guidance in selecting the poles for preventive replacement under normal operating circumstances.

Probabilistic Modeling of Corrosion Deterioration and Aging in Structures

Corrosion of steel reinforcement in concrete structures is a hard-to-predict and serious deterioration state that can be the cause of structural problems ranging from aesthetic surface cracking to significant loss of load capacity and the potential for brittle structural failure.  In marine environments with exposure to highly saline ambient conditions, chloride-induced corrosion is an especially serious threat to structural reliability and safety in reinforced and prestressed concrete structures.  However, it is very difficult to produce clear and accurate estimates and predictions of the extent of corrosion deterioration due to its dependency on highly variable parameters having to do with outside environment, structural geometry, and materials present, in addition to other factors.  Gaining a more complete understanding of these variables and finding useful ways to apply them in finite element modeling and numerical simulation will allow for more accurate and realistic analysis of aging structures and how, where, and when corrosion deterioration affects them.  The goal of this research is to integrate multiple empirically and statistically verified components of chloride-induced corrosion into one comprehensive model, and to couple them with mechanistic models of structures. This creates a detailed framework that reveals novel information on how these interacting factors affect reliability of structures subject to corrosion. The result will be reliable prediction models for the performance of structures that can be used in risk assessment and management of aging infrastructure systems.