Optimal Design of Composite Channels Using Genetic Algorithm

In the past, studies involving optimal design of composite channels have employed Horton’s equivalent roughness coefficient, which uses a lumped approach in assuming constant velocity across a composite channel cross section. In this paper, a new nonlinear optimization program (NLOP) is proposed based on a distributed approach that is equivalent to Lotter’s observations, which allows spatial variations in velocity across a composite channel cross section. The proposed NLOP, which consists of an objective function of minimizing total construction cost per unit length of a channel, is solved using genetic algorithm (GA). Several scenarios are evaluated, including no restrictions, restricted top width, and restricted channel side slopes, to account for certain site conditions. In addition, the proposed NLOP is modified to include constraints on maximum permissible velocities corresponding to different lining materials of the composite channel cross section, probably for the first time. The proposed methodolo...

[1]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[2]  G. V. Loganathan,et al.  Optimal Design of Parabolic Canals , 1991 .

[3]  David C. Froehlich,et al.  Width and Depth‐Constrained Best Trapezoidal Section , 1994 .

[4]  Amlan Das,et al.  Optimal Channel Cross Section with Composite Roughness , 2000 .

[5]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

[6]  V. T. Chow.  Open-channel hydraulics , 1959 .

[7]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Erik D. Goodman,et al.  Genetic Learning Procedures in Distributed Environments , 1987, ICGA.

[10]  Hans A. Einstein,et al.  River Channel Roughness , 1952 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  David E. Goldberg,et al.  Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy , 1987, ICGA.

[13]  Parviz Monadjemi,et al.  General Formulation of Best Hydraulic Channel Section , 1989 .

[14]  B. Yen.  Open Channel Flow Resistance , 2002 .

[15]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[16]  Muthusamy Krishnamurthy,et al.  Equivalent Roughness for Shallow Channel , 1972 .

[17]  S. Liong,et al.  Peak-flow forecasting with genetic algorithm and SWMM , 1995 .

[18]  Thomas J. Trout,et al.  Channel Design to Minimize Lining Material Costs , 1982 .

[19]  Charles V. Camp,et al.  Use of GA to determine areas of accretion to semiconfined aquifer , 2001 .

[20]  W. C. Hughes,et al.  Optimal Channel Cross Section with Freeboard , 1984 .

[21]  J. Eheart,et al.  Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem , 1995 .

[22]  Prabhata K. Swamee,et al.  Design of Minimum Seepage Loss Canal Sections , 2001 .

[23]  Richard C. Peralta,et al.  Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm , 1999 .

[24]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[25]  Andrew Mcgilvary Gillies,et al.  Machine Learning Procedures for Generating Image Domain Feature Detectors , 1985 .