Optimum geometrical pattern and design of real-size diagrid structures using accelerated fuzzy-genetic algorithm with bilinear membership function

Abstract Diagrids are the efficient systems of tube structures for tall buildings. One of the design considerations for these structures is the geometrical pattern of the system. In this paper, a new method of fuzzy-genetic algorithm based on bilinear membership functions is proposed with an improved crossover operator and penalty function. The method is applied on tall buildings with a diagrid system to find the optimum geometrical patterns and the overall structural weight. Various three-dimensional diagrid structures with 24, 36, 42, 56, and 60 stories and different slenderness ratios are analyzed under gravity and wind load. Then the effects of variation in the number of bays (4, 6, and 8) are investigated and compared with each other. The results show that by increasing the dimension of the structure, the structural weight is reduced up to 33% in some cases. However, the obtained angle of the diagrid members (range of 63 to 79 degrees) is increased by increasing the number of stories and the height of the structure. The optimum weight and geometrical pattern of the models is obtained and a formulation is extracted from the results regarding the optimum angle of a diagrid system. Considering GA, results show the merit of the accelerated fuzzy-genetic algorithm regarding the convergence and the avoidance of being trapped in local minimum.

[1]  Payam Ashtari,et al.  Using fuzzy genetic algorithm for the weight optimization of steel frames with semi-rigid connections , 2015 .

[2]  R. Gnanadass,et al.  Optimization of process parameters through fuzzy logic and genetic algorithm - A case study in a process industry , 2015, Appl. Soft Comput..

[3]  R. J. Kuo,et al.  Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering , 2015, Appl. Soft Comput..

[4]  Giuseppe Brandonisio,et al.  Design criteria for diagrid tall buildings: Stiffness versus strength , 2014 .

[5]  Panos M. Pardalos,et al.  hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems , 2012, Appl. Soft Comput..

[6]  Giuseppe Brandonisio,et al.  Geometrical patterns for diagrid buildings: Exploring alternative design strategies from the structural point of view , 2014 .

[7]  Agustín Jiménez,et al.  Multidimensional membership functions in T-S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms , 2019, Appl. Soft Comput..

[8]  Young Ju Kim,et al.  Cyclic Behavior of Diagrid Nodes with H-Section Braces , 2010 .

[9]  Feng Fu.  Bracing, Diagrid, 3D Space Frame, and Mega Frame Structural Systems in Tall Buildings , 2018 .

[10]  Kaiqiang Ma,et al.  Calculation model of the lateral stiffness of high‐rise diagrid tube structures based on the modular method , 2017 .

[11]  George K. I. Mann,et al.  Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots , 2008, Appl. Soft Comput..

[12]  Kenny C. S Kwok,et al.  Occupant comfort evaluation and wind-induced serviceability design optimization of tall buildings , 2011 .

[13]  Fabrizio Mollaioli,et al.  Diagrid structural systems for tall buildings: Changing pattern configuration through topological assessments , 2017 .

[14]  Genshu Tong,et al.  Performance‐based plastic design and collapse assessment of diagrid structure fused with shear link , 2019, The Structural Design of Tall and Special Buildings.

[16]  Jaehong Lee,et al.  Investigation on material layouts of structural Diagrid frames by using topology optimization , 2014 .

[17]  Dongkyu Lee,et al.  Optimized topology extraction of steel-framed DiaGrid structure for tall buildings , 2010 .

[18]  Feng Zhang,et al.  Simplified calculation of shear lag effect for high-rise diagrid tube structures , 2019, Journal of Building Engineering.

[19]  Hojjat Adeli,et al.  Diagrid: An innovative, sustainable, and efficient structural system , 2017 .

[21]  Chih-Hsun Chou,et al.  Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction , 2017, Appl. Soft Comput..

[22]  Wei Zhou,et al.  Experiment and Analysis on X-Shaped Reinforced Concrete Joint in Diagrid Structures , 2013 .

[23]  Payam Ashtari,et al.  Using fuzzy genetic, Artificial Bee Colony (ABC) and simple genetic algorithm for the stiffness optimization of steel frames with semi-rigid connections , 2015 .

[24]  Yansheng Liu,et al.  Diagrid tube structures composed of straight diagonals with gradually varying angles , 2012 .

[25]  Young-Ho Lee,et al.  Seismic Performance Evaluation of Diagrid System Buildings , 2012 .

[26]  Paresh V. Patel,et al.  Analysis and Design of Diagrid Structural System for High Rise Steel Buildings , 2013 .

[27]  Yue Li,et al.  Seismic Performance Assessment and Loss Estimation of Steel Diagrid Structures , 2018, Journal of Structural Engineering.

[28]  Ghader Karimian,et al.  Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems , 2017, Appl. Soft Comput..

[29]  Matin Alaghmandan,et al.  Mutual effect of geometric modifications and diagrid structure on structural optimization of tall buildings , 2018 .

[30]  Soomi Shin,et al.  Advanced high strength steel tube diagrid using TRIZ and nonlinear pushover analysis , 2014 .

[31]  Iraj Mahdavi,et al.  A genetic algorithm for optimization problems with fuzzy relation constraints using max-product composition , 2011, Appl. Soft Comput..

[32]  Payam Ashtari,et al.  Simultaneous size and topology optimization of 3D outrigger‐braced tall buildings with inclined belt truss using genetic algorithm , 2020, The Structural Design of Tall and Special Buildings.

[33]  Jing Liu,et al.  Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm , 2019, Appl. Soft Comput..

[34]  R. J. Kuo,et al.  Partition-and-merge based fuzzy genetic clustering algorithm for categorical data , 2019, Appl. Soft Comput..

[35]  T. S. B. Sudarshan,et al.  A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP) , 2015, Appl. Soft Comput..

[36]  Young-Kyu Ju,et al.  Finite element analysis of optimized brace angle for the diagrid structural system , 2016 .

[37]  Hojjat Adeli,et al.  Seismic performance factors for low- to mid-rise steel diagrid structural systems , 2018, The Structural Design of Tall and Special Buildings.

[38]  Jerome J. Connor,et al.  Diagrid structural systems for tall buildings: characteristics and methodology for preliminary design , 2007 .

[39]  S. Muthukrishnan,et al.  AGFS: Adaptive Genetic Fuzzy System for medical data classification , 2014, Appl. Soft Comput..

[40]  Wei Zhou,et al.  Experiment and analysis on reinforced concrete spatial connection in diagrid tube , 2016 .

[41]  Kyoung Sun Moon.  Diagrid Structures for Complex-Shaped Tall Buildings , 2011 .

[42]  Antonello Rizzi,et al.  Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids , 2016, Appl. Soft Comput..

[43]  Fayaz R. Rofooei,et al.  Quantification of the seismic performance factors for steel diagrid structures , 2018 .

[44]  A. Aghakouchak,et al.  Quantification of seismic performance factors of steel diagrid system , 2018, The Structural Design of Tall and Special Buildings.

[45]  Damjan Strnad,et al.  A fuzzy-genetic decision support system for project team formation , 2010, Appl. Soft Comput..

[46]  Jinkoo Kim,et al.  Progressive collapse behavior of rotor-type diagrid buildings , 2012 .

[47]  Feng Zhao,et al.  Diagonal arrangements of diagrid tube structures for preliminary design , 2015 .

[48]  Xiaohong Chen,et al.  A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process , 2015, Appl. Soft Comput..

[49]  Kyoung Sun Moon,et al.  Diagrid Structural System for High-Rise Buildings: Applications of a Simple Stiffness-based Optimized Design , 2016 .

[50]  Frederico G. Guimarães,et al.  A fuzzy genetic algorithm for automatic orthogonal graph drawing , 2012, Appl. Soft Comput..

[51]  Elena Mele,et al.  Optimization of structural patterns for tall buildings: The case of diagrid , 2018, Engineering Structures.

[52]  Payam Ashtari,et al.  Accelerating fuzzy genetic algorithm for the optimization of steel structures , 2012 .

[53]  Hyun-Su Kim,et al.  MOGA-Based Structural Design Method for Diagrid Structural Control System Subjected to Wind and Earthquake Loads , 2018 .

[54]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems , 2015, Appl. Soft Comput..

[55]  Seong-Hui Lee,et al.  Mitigation of stress concentration in a diagrid structural system using circular steel tubes , 2015 .

[56]  David García,et al.  An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm , 2019, Appl. Soft Comput..

[57]  Giuseppe Brandonisio,et al.  Diagrid structures for tall buildings: case studies and design considerations , 2014 .

[58]  Payam Ashtari,et al.  Weight and topology optimization of outrigger-braced tall steel structures subjected to the wind loading using GA , 2015 .

[59]  Amin Ghodousian,et al.  An efficient genetic algorithm for solving nonlinear optimization problems defined with fuzzy relational equations and max-Lukasiewicz composition , 2018, Appl. Soft Comput..

[60]  Ricardo Tanscheit,et al.  GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems , 2015, Appl. Soft Comput..

[61]  Zheng Lu,et al.  A review of the diagrid structural system for tall buildings , 2018 .