In order to overcome negative consequences of a disaster, buildings and infrastructures need to be resilient. After a disaster occurs, they must get back to their normal operations as quickly as possible. Buildings and infrastructures should incorporate both pre-event (preparedness and mitigation) and post-event (response and recovery) resilience activities to minimize negative effects of a disaster. Quantitative approaches for measuring resilience for buildings and infrastructures need to be developed. A proposed methodology for quantification of resilience of a given building type based on different hurricane categories is presented. The formulation for the resilience quantification is based on a model embedding several distinct parameters (for example, structural loss ratios, conditional probabilities of exceeding for damage states, estimated and actual recovery times, wind speed probability). The proposed resilience formulation is applied to a residential building type selected from HAZUS. Numerical results of resilience for the selected residential building type against Category 1, 2, and 3 hurricanes are presented in a dashboard representation. Resilience performance indicators between different types of buildings, which are identical except for their roof types, have been evaluated in order to present applicability of the proposed methodology.
The objective of this study was to develop practical scheduling solutions for chemical tankers visiting the Port of Houston (PoH). Chemical tanker movements represent approximately 42% of the Houston Ship Channel traffic. Historically, chemical tanker scheduling has been problematic and has resulted in long waiting times for tankers. Scheduling is difficult because chemical tankers carry several liquid cargoes and must visit multiple terminals for loading and unloading. Physical constraints (layout of the port and draft) and commercial constrains (such as terminal and personnel readiness for cargo handling operations, tank cleaning processes, and inspection requirements) create a complex scheduling problem, long waiting times, and unnecessary tanker movements in the port. These problems cause an increase in the business costs for shipowners, risk of collisions and allisions, production of additional air emissions, and decreases in the operating capacity of terminals. The recent expansion decisions for chemical and petrochemical plants in Houston, Texas, will exacerbate the problem. Significant benefits could thus be gained even for small scheduling improvements. Currently, the scheduling practice of loading/unloading activities in the PoH involves primarily the manual and de-centralized use of the "first come, first served" (FCFS) rule, which results in inefficiencies such as long waiting times and poor resource utilization. We propose two mathematical methods to address the tanker scheduling problem in the port: a mixed-integer programming (MIP) method, and a constraint programming (CP) method. The two methods are formulated as open-shop scheduling problems with sequence-dependent post-setup times. MIP yields optimum results that minimize makespan. However, computation time increases significantly as the number of tankers, or the number of terminals, increases. CP achieves better makespan results in a shorter run time, compared to MIP, for medium to large-scale problems including the problem considered in this case study. Overall, the results show that MIP is more suitable for real-time scheduling tools (hourly and daily), whereas CP is the better option for longer-horizon scheduling problems (weekly or monthly). Our models gave good alternative schedules under short optimization run times. Hence, they can afford decision makers sufficient time to complete multiple optimization scenarios and implementation setups.
The utility poles of electric power distribution lines are very vulnerable to many natural hazards, while power outages due to pole failures can lead to adverse economic and social consequences. Utility companies, therefore, need to monitor the conditions of poles regularly and predict their future conditions accurately and promptly to operate the distribution system continuously and safely. This article presents a novel pole monitoring method that uses state-of-the-art deep learning and computer vision methods to meet the need. The proposed method automatically captures the current pole inclination angles using an unmanned aerial vehicle. The method calculates the bending moment exerted on the poles due to wind and gravitational forces, as well as cable weight, to compare it with the moment of rupture. The method also includes a machine learning-based model that is built by using a support vector machine to predict the resilience conditions of a pole after a wind event in a faster manner. The three modules of the proposed method are effective tools to classify pole conditions and are expected to enable utility companies to increase the resilience of their systems.
The utility poles of an electric power distribution system are frequently damaged by wind-related disasters. This study notes that the wooden poles are particularly vulnerable to such disasters and the failures of the poles can cause a network-level failure leading to short-or longterm power outages. To mitigate the problem, this study proposes a framework for measuring the resilience of the wooden utility poles based on the angular deflection of a pole due to the wind force. Given the existing inclination angle of a pole, the angular deflection is measured by finite element analysis using ANSYS Ò Workbench 1 to determine the resilience area under various wind speeds. For this, the conditions of load and support for a pole, which are called boundary conditions in ANSYS Ò , are generated. The proposed framework also includes an approach to cost-benefit analysis that compares different strategies for corrective action. The results of the case study in which the framework was applied show that the proposed framework can be effectively utilized by electric power distribution companies to increase the resilience of their systems.
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