Donghwi Jung

Seismic Hazard Assessment Model for Urban Water Supply Networks

AbstractA new seismic reliability evaluation model is proposed that quantifies the impact of earthquakes on hydraulic behavior of water supply networks. Probabilistic seismic events are produced in the target areas, and the depth of earthquake failure is evaluated by seismic reliability indicators. The developed model was applied to several case studies and used for an intensive examination on how a water supply system hydraulically responds to a seismic event and what system characteristics influence the system’s performance in the event of an earthquake. First, the system reliability of a real network in South Korea when subjected to earthquakes of various magnitudes and locations was quantified. Next, the reliabilities of full and simplified network models were evaluated to investigate how system layouts affect the reliability evaluation. Finally, networks with different configurations, pipe sizes, and system densities were compared with respect to the seismic reliability and various seismic damage ind...

Investigating the impact of proactive pump operation and capacity expansion on urban drainage system resilience

AbstractThe individual and combined effects of structural and nonstructural measures on urban drainage system resilience were investigated in this study. The resilience of an urban drainage system ...

Optimal design of water distribution systems considering uncertainties in demands and roughness coefficients

The optimal design of water distribution system has been usually performed with fixed hydraulic variables and single objective. However, a realistic water distribution system should take inherent uncertainties of data into consideration. This study suggests a method to minimize the system cost and maximize the robustness of network based on uncertainties in nodal demands and pipe roughness coefficients. Multi-Objective Genetic Algorithm (MOGA) implements two separate optimization models for the least cost and the best robustness design as the initial population. The model considers the uncertainties in roughness coefficient and water demand by using Latin Hypercube sampling technique with the assumption of beta probability density function. Several beta probability density functions with wide range of data are evaluated in the procedure. The proposed approach is tested in case study of the

A New Parallelization Scheme for Harmony Search Algorithm

During the last two decades, parallel computing has drawn attention as an alternative to lessen computational burden in the engineering domain. Parallel computing has also been adopted for meta-heuristic optimization algorithms which generally require large number of functional evaluations because of their random nature of search. However, traditional parallel approaches, which distribute and perform fitness calculations concurrently on the processing units, are not intended to improve the quality of solution but to shorten CPU computation time. In this study, we propose a new parallelization scheme to improve the effectiveness and efficiency of harmony search. Four harmony searches are simultaneously run on the processors in a work station, sharing search information (e.g., a good solution) at the predefined iteration intervals. The proposed parallel HS is demonstrated through the optimization of an engineering planning problem.

State estimation network design for water distribution systems

AbstractState estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often ...

KU Battle of Metaheuristic Optimization Algorithms 1: Development of Six New/Improved Algorithms

Each of six members of hydrosystem laboratory in Korea University (KU) invented either a new metaheuristic optimization algorithm or an improved version of some optimization methods as a class project for the fall semester 2014. The objective of the project was to help students understand the characteristics of metaheuristic optimization algorithms and invent an algorithm themselves focusing those regarding convergence, diversification, and intensification. Six newly developed/improved metaheuristic algorithms are Cancer Treatment Algorithm (CTA), Extraordinary Particle Swarm Optimization (EPSO), Improved Cluster HS (ICHS), Multi-Layered HS (MLHS), Sheep Shepherding Algorithm (SSA), and Vision Correction Algorithm (VCA). This paper describes the details of the six developed/improved algorithms. In a follow-up companion paper, the six algorithms are demonstrated and compared through well-known benchmark functions and a real-life engineering problem.

Optimal Life Cycle design of Water Pipe System using Genetic Algorithm

In this study, a model is developed based on Life Cycle Energy Analysis (LCEA) method with Genetic Algorithm (GA) to determine optimal diameter of Water Distribution System (WDS). For hydraulic analysis the EPANET2.0 program is linked with developed model, pipe-aging equation and pipe-breakage equation are built in to developed model to simulate pipe change through life cycle. The model is then applied to two sample WDSs for optimal energy design. After determining optimal diameter for each WDS, the total cost is calculated based on determined diameter and compared with well-known optimal diameter set of each WDS. Results show that optimal energy design of WDSs through the developed model can be an alternative option for optimal design of WDSs for reducing energy with lower in cost.

Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks

Various metaheuristic optimization algorithms are being developed and applied to find optimal solutions of real-world problems. Engineering benchmark problems have been often used for the performance comparison among metaheuristic algorithms, and water distribution system (WDS) design problem is one of the widely used benchmarks. However, only few traditional WDS design problems have been considered in the research community. Thus, it is very challenging to identify an algorithm’s better performance over other algorithms with such limited set of traditional benchmark problems of unknown characteristics. This study proposes an approach to generate WDS design benchmarks by changing five problem characteristic factors which are used to compare the performance of metaheuristic algorithms. Obtained optimization results show that WDS design benchmark problems generated with specific characteristic under control help identify the strength and weakness of reported algorithms. Finally, guidelines on the selection of a proper algorithm for WDS design problems are derived.

An Evolutionary Algorithm Based Hyper-heuristic for the Set Packing Problem

Utilizing knowledge of the problem of interest and lessons learned from solving similar problems would help to find the final optimal solution of better quality. A hyper-heuristic algorithm is to gain an advantage of such process. In this paper, we present an evolutionary algorithm based hyper-heuristic framework for solving the set packing problem (SPP). The SPP is a typical \(\mathcal {NP}\)-hard problem. The hyper-heuristic is comprising of high level and low level. The higher level is mainly engaged in generating or constructing a heuristic. An evolutionary algorithm with guided mutation (EA/G) is employed at the high level. Whereas a set of problem-independent and problem-specific heuristics, called low level heuristics, are employed at the low level of hyper-heuristic. EA/G is recently added to the class of the evolutionary algorithms that try to utilize the complementary characteristics of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) to generate new offspring. In EA/G, the guided mutation operator generates an offspring by sampling the probability vector. The proposed approach is compared with the state-of-the-art approaches reported in the literature. The computational results show the effectiveness of the proposed approach.

Urban Drainage System Design Minimizing System Cost Constrained to Failure Depth and Duration Under Flooding Events

Recently, property damages and loss of life caused by natural disasters are increasing in urban area because of local torrential rainfall, which is mostly originated from recent global climate change. Acceleration of population concentration and increase of impervious area from urbanization worsen the situation. Therefore, it is highly important to consider system resilience which is the system’s ability to prepare, react, and recover from a failure (e.g., flooding). This study proposes a resilience-constrained optimal design model of urban drainage network, which minimizes total system cost while satisfying predefined failure depth and duration (i.e., resilience measures). Optimal layout and pipe sizes are identified by the proposed model comprised of Harmony Search Algorithm (HSA) for optimization and Storm Water Management Model (SWMM) for dynamic hydrology-hydraulic simulation. The proposed model is applied to the design of Gasan urban drainage system in Seoul, Korea, and the resilience-based design obtained is compared to the least-cost design obtained with no constraint on the resilience measures.