A proposal for weighted optimization method on bridge health integrity through corroded steel bridge infrastructure

2014 ◽  
pp. 1495-1502
Author(s):  
S Murakami ◽  
I Hosoe
Keyword(s):  
Optimization Method ◽  
Steel Bridge ◽  
Weighted Optimization

scholarly journals Structural Optimization of Steel—Epoxy Asphalt Pavement Based on Orthogonal Design and GA—BP Algorithm

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 417
Author(s):  
Xunqian Xu ◽  
Yuwen Gu ◽  
Wei Huang ◽  
Dakai Chen ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Stress Concentration ◽  
Asphalt Pavement ◽  
Fatigue Cracks ◽  
Design Strategy ◽  
Optimization Method ◽  
Maximum Point ◽  
Steel Bridge ◽  
Epoxy Asphalt ◽  
Longitudinal Stiffeners ◽  
Pavement Structure

Fatigue cracks often occur in the deck asphalt pavement of steel bridges at the top of the longitudinal stiffening rib. To prevent this issue, the traditional design strategy of the steel bridge deck asphalt pavement structure was optimized, and a new approach is presented. This optimization technique exploits the strength simulation of the steel—epoxy asphalt pavement structure, and the stress concentration location is subsequently determined. A solid model of stress concentration including sensitive areas is then established. We examined the stress maximum point of the asphalt pavement layer at the top of the longitudinal stiffeners and the stress variation of the asphalt pavement layer at the top of the longitudinal stiffeners. To reduce the stress of the top pavement layer of the longitudinal stiffeners, an optimization method that combines orthogonal experimental design, neural network (BP), and genetic algorithm (GA) is presented. A design strategy for the steel—epoxy asphalt pavement structure and GA—BP optimization method was utilized to optimize the structure of the steel—epoxy asphalt pavement for Sutong Yangzi River Bridge. We confirmed that the presented approach improved fatigue reliability and established the efficacy of the design strategy and optimization method.

Optimization Method of Location for Cooperative Lifting of Two Mobile Cranes

2012 ◽  
Author(s):  
Xin Wang ◽  
Yuanyuan Liu ◽  
Fang Liu
Keyword(s):  
Collision Detection ◽  
Optimization Method ◽  
Step Length ◽  
Optimal Location ◽  
Bisection Method ◽  
Important Work ◽  
Optimization Function ◽  
Search Field ◽  
Weighted Optimization ◽  
Work Envelope

Dual-crane lifting has been generally used with the need of erection and installation of large equipment. Choosing proper locations for two mobile cranes is an important work as well as a difficulty in design of heavy lifting plan. So, this paper proposes an optimization method of location for cooperative lifting of dual-crane. This approach starts with determining search field and maximum step length, and then finds out load’s possible initial locations by bisection method; secondly calculates work envelope of dual-crane which will be dispersed to find all dual-crane’s possible locations; after this, it puts cranes in their respective possible locations corresponding to each load’s location and simulates the whole lifting course while carries out collision detection to exclude crash happened locations; finally, creates weighted optimization function, uses enumeration method to traverse all collision-free locations, and gains optimal location of dual-crane and load. At last, this approach has been used in an actual engineering case and optimal location is got, from which we can see its feasibility and validity.

scholarly journals Optimized resource-constrained method for project schedule compression

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Moaaz Elkabalawy ◽  
Osama Moselhi
Keyword(s):  
Genetic Algorithm ◽  
Resource Planning ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimum ◽  
Project Schedule ◽  
Nsga Ii ◽  
Content Type ◽  
Modeling Uncertainties ◽  
Weighted Optimization

PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

Optimization Method of Superelevation Transition for Oval Racing Tracks

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Yuchen Wang ◽  
Tao Lu ◽  
Hongxing Zhao ◽  
Zhiying Bao
Keyword(s):  
Optimization Method

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

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