Herbert Martins Gomes

Truss optimization with dynamic constraints using a particle swarm algorithm

In this paper, a structural truss mass optimization on size and shape is performed taking into account frequency constraints. It is well-known that structural optimizations on shape and size are highly non-linear dynamic optimization problems since this mass reduction conflicts with the frequency constraints especially when they are lower bounded. Besides, vibration modes may switch easily due to shape modifications. This paper intends to investigate the use of a particle swarm optimization (PSO) algorithm as an optimization engine in this type of problem. This choice is based on reported well-behavior of such algorithm as global optimizer in other areas of knowledge. Another feature of the algorithm is taken into account for this choice, like the fact that it is not gradient based, but just based on simple objective function evaluation. The algorithm is briefly revised highlighting its most important features. It is presented four examples regarding the optimization of trusses on shape and size with frequency constraints. The examples are widely reported and used in the related literature as benchmarks. The results show that the algorithm performed similar to other methods and even better in some cases.

A firefly metaheuristic structural size and shape optimisation with natural frequency constraints

A structural mass optimisation on shape and size is performed in this paper taking into account natural frequency constraints. Mass reduction conflicts with frequency constraints when they are lower bounded since vibration mode shapes may easily switch due to shape modifications. Here, it is investigated the use of the firefly metaheuristic algorithm (FMA) as an optimisation engine. One important feature of the algorithm is the non-gradient based evaluations, but on single objective function evaluations. This is of paramount importance when dealing with non-linear optimisation problems with several constraints avoiding bad numerical behaviour due to gradient evaluations. The algorithm is revised, highlighting its most important features. It is suggested some new implementations of the basic algorithm based on literature reports in order to improve its performance. The paper presents several examples regarding the optimisation on shape and sizing with natural frequency constraints of complex trusses that are widely reported in the literature as benchmark examples solved with several non-heuristic and heuristic algorithms. The results show that the algorithm outperforms deterministic algorithms but behaves similar to other metaheuristic methods.

Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm

The purpose of this paper is to determine the lumped suspension parameters that minimise a multi-objective function in a vehicle model under different standard PSD road profiles. This optimisation tries to meet the rms vertical acceleration weighted limits for human sensitivity curves from ISO 2631 [ISO-2631: guide for evaluation of human exposure to whole-body vibration. Europe; 1997] at the drivers seat, the road holding capability and the suspension working space. The vehicle is modelled in the frequency domain using eight degrees of freedom under a random road profile. The particle swarm optimisation and sequential quadratic programming algorithms are used to obtain the suspension optimal parameters in different road profile and vehicle velocity conditions. A sensitivity analysis is performed using the obtained results and, in Class G road profile, the seat damping has the major influence on the minimisation of the multi-objective function. The influence of vehicle parameters in vibration attenuation is analysed and it is concluded that the front suspension stiffness should be less stiff than the rear ones when the drivers seat relative position is located forward the centre of gravity of the car body. Graphs and tables for the behaviour of suspension parameters related to road classes, used algorithms and velocities are presented to illustrate the results. In Class A road profile it was possible to find optimal parameters within the boundaries of the design variables that resulted in acceptable values for the comfort, road holding and suspension working space.

Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements

Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, Artificial Neural Networks (ANN) may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and ANN to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to solve optimization problems based in frequencies and modal criteria, and a perceptron ANN module is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.

Reliability analysis of concrete structures with neural networks and response surfaces

Purpose – To research the feasibility in using artificial neural networks (ANN) and response surfaces (RS) techniques for reliability analysis of concrete structures.Design/methodology/approach – The evaluation of the failure probability and safety levels of structural systems is of extreme importance in structural design, mainly when the variables are eminently random. It is necessary to quantify and compare the importance of each one of these variables in the structural safety. RS and the ANN techniques have emerged attempting to solve complex and more elaborated problems. In this work, these two techniques are presented, and comparisons are carried out using the well‐known first‐order reliability method (FORM), with non‐linear limit state functions. The reliability analysis of reinforced concrete structure problems is specially considered taking into account the spatial variability of the material properties using random fields and the inherent non‐linearity.Findings – It was observed that direct Monte...

Investigation of the mechanical behavior of trapezoidal roofing sheets using genetic algorithms

The steel profiled sheets are an economical alternative for roofing and siding in industrial stores, hangars and other kinds of pavilions, since these structural components are able to close large openings without too much increasing of the load over the cladding system structures. Trapezoidal sheets are specifically a good alternative to close this kind of structures. The opening span that this kind of sheet is able to cover can be determined through the serviceability displacement limit state. Since the steel sheet cross-sectional elements have little thickness, the local buckling collapse modes must be considered in this calculus. With this study, we attempt to evaluate the best alternatives for trapezoidal-shaped cross-sections of steel sheets to be used in roof cladding, subjected to bending moments. The sheets analysis has been carried out by using three-dimensional shell finite element models combined with optimization routines based on genetic algorithms procedures. The first profile to be obtained has been the one that has showed the lowest displacement and the highest critical elastic buckling load, at the same time maximizing the roof covering area. Finally, as a result of a sensitivity analysis, it has been possible to state some useful criteria for the trapezoidal steel roofing sheets design by modifying the weights of the proposed optimization objectives.

Multi-objective optimization of quarter car passive suspension design in the frequency domain based on PSO

Purpose – The purpose of this paper is to investigate the optimum design of a quarter car passive suspension system using a particle swarm optimization algorithm in order to minimize the applied loads and vibrations. Design/methodology/approach – The road excitation is assumed as zero-mean random field and modeled by single-sided power spectral density (PSD) based on international standard ISO 8608. The variance of sprung mass displacements and variance of dynamic applied load are evaluated by PSD functions and used as cost function for the optimization. Findings – The advantages in using this methodology are emphasized by an example of the multi-objective optimization design of suspension parameters and the results are compared with values reported in the literature and other gradient based and heuristic algorithms. The paper shows that the algorithm effectively leads to reliable results for suspension parameters with low computational effort. Research limitations/implications – The procedure is applied to a quarter car passive suspension design. Practical implications – The proposed procedure implies substantial time savings due to frequency domain analysis. Social implications – The paper proposes a procedure that allows complex optimization designs to be feasible and cost effective. Originality/value – The design optimization is performed in the frequency domain taking into account standard defined road profiles PSD without the need to simulate in the time domain.

Measurement and evaluation of human exposure to vibration transmitted to hand-arm system during leisure cyclist activity

INTRODUCTION: The cycling activity has increased in recent years, either as a means of leisure or physical activity or as means of transport. Discomfort is one of the main complaints for cyclists, especially when related to the type o pavement they use while riding. This work presents a study of measurement and evaluation of human exposure to hand-arm vibration in the leisure cyclist activity in different pavements in order to classify according to vibration discomfort and to vibration injury risk. METHODS: Vibration measurements are performed for three pavement types, asphalt (AS), precast concrete slab (PC), and interlocking concrete blocks (BI), using two bicycle models (time trial speed racing bike, S and mountain bike, MB), and cyclists with different physical characteristics. It is performed a quantitative analysis of each configuration - pavement type × bike model × cyclist - where the daily vibration exposure A(8) is evaluated, as defined in ISO 5349-1 Standard, for 2h daily exposure. It is also evaluated the maximum daily exposure in order to reach limit values, as defined by Directive 2002/44/EC. RESULTS: Based on a subjective analysis (survey), it is evaluated the comfort degree for vibration exposure for each tested pavement, according to a survey within cyclists. Finally, the results are compared using both quantitative and subjective analysis. CONCLUSIONS: Not surprisingly, it has been noticed that the most comfortable pavement type is the asphalt pavement (AS), followed by the precast concrete pavement (PC) and by the interlocking concrete blocks pavement (BI), confirming the opinion pool within cyclists. As a new finding, for some pavement types, bikes and daily journey activities, the vibration levels may reach health limit levels which justify the originality of the work and the importance as guidance for healthy public decisions for new cycle paths.

Comparative analysis between efficiency grouping and efficacy grouping in cell formation using the firefly metaheuristic algorithm

Industrial environments can use several layout setups according to their convenience based on item diversity, ways of production and market demand. This work focuses on design of manufacturing cell formation. A well-known type of production layout in industrial engineering that allows meeting a diversity of production requirements and operational flexibility is the cellular manufacturing. Among the various techniques and approaches applied to manufacturing cell formation, this article uses the firefly metaheuristic algorithm as an optimization engine. Such modern stochastic optimization algorithm can tackle non-convex, non-smooth, non-continuous and non-differentiate objective cost functions making them gradient independent and suitable to manage such problems. The adopted methodology relies on comparing the obtained efficiency and efficacy (clustering) parameters in the cell formation layout with several well-established benchmark examples found in the literature. The results show that the use of efficacy parameter is desirable if the focus relies on clustering manufacturing cell formation, however, the use of efficiency parameter can also lead to useful and cost-effective layouts at the expense of cell clustering. Some of the results also indicate improvements in the efficacy parameter relative to the benchmarked examples with a slightly enhanced layout formation that proves and justifies the suitability for the firefly metaheuristic algorithm.

Model Updating by Uncertain Parameter Inference

The model updating based on measured data is a challenging problem that has received attention and resulted in different approaches. Depending on the intended level of accuracy and availability of data, the adoption of right approach may bring important information to the updated parameters and ultimately may reduce the systematic (Epistemic) uncertainty and quantify the irreducible statistical uncertainty (Aleatory). This paper explains, applies and compares three model updating techniques (i.e. Sensitivity model updating, Interval model updating and Bayesian model updating) to simple numerical examples. Remarks related to the performance, accuracy and information provided by the different approaches are drawn along the examples.