Constrained multi-objective optimization algorithms: Review and comparison with application in reinforced concrete structures

Engineering design problems are often multi-objective in nature, which means trade-offs are required between conflicting objectives. In this study, we examine the multi-objective algorithms for the optimal design of reinforced concrete structures. We begin with a review of multi-objective optimization approaches in general and then present a more focused review on multi-objective optimization of reinforced concrete structures. We note that the existing literature uses metaheuristic algorithms as the most common approaches to solve the multi-objective optimization problems. Other efficient approaches, such as derivative-free optimization and gradient-based methods, are often ignored in structural engineering discipline. This paper presents a multi-objective model for the optimal design of reinforced concrete beams where the optimal solution is interested in trade-off between cost and deflection. We then examine the efficiency of six established multi-objective optimization algorithms, including one method based on purely random point selection, on the design problem. Ranking and consistency of the result reveals a derivative-free optimization algorithm as the most efficient one.     

Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Concrete carbonation is one of the main causes of corrosion of the reinforcement and consequently causing damage to the reinforced concrete structures. The progress of the carbonation front depends on many factors including mixture proportions and exposure conditions. Several carbonation prediction models including mathematical and analytical predictions are available. Most of these models, however, are based on simple regression equations and cannot predict or accurately reflect the various factors involved in concrete carbonation. The current published results in this issue are in conflict. In view of this, our research aims to apply an artificial neural network (ANN) approach for predicting the carbonation of fly-ash concrete taking into account the most influential parameters, including mixture proportions and exposure conditions. Six parameters were considered as inputs to the ANN model, covering, binder and fly-ash content, water-to-binder ratio, CO₂ concentration, relative humidity, and time of exposure; one output is carbonation depth. The ANN model was prepared, trained, and tested with 300 datasets from experiments as well as past research. The performance of training, validation, and test sets shows a high correlation between the experimental and the ANN predicted values of the carbonation depth. In addition, the proposed prediction model was in good agreement with the experimental data in comparison with other model. This study concludes that the use of this model for numerical investigations on the parameters affecting the carbonation depth in fly-ash concrete is successful and provides scientific guidance for durability design.

     

Multi-objective optimisation and decision-making of space station logistics strategies

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers’ preferences.     

A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

This paper aims to develop a practical artificial neural network (ANN) model for predicting the punching shear strength (PSS) of two-way reinforced concrete slabs. In this regard, a total of 218 test results collected from the literature were used to develop the ANN models. Accordingly, the slab thickness, the width of the column section, the effective depth of the slab, the reinforcement ratio, the compressive strength of concrete, and the yield strength of reinforcement were considered as input variables. Meanwhile, the PSS was considered as the output variable. Several ANN models were developed, but the best model with the highest coefficient of determination (R²) and the smallest root mean square errors was retained. The performance of the best ANN model was compared with multiple linear regression and existing design code equations. The comparative results showed that the proposed ANN model was provided the most accurate prediction of PSS of two-way reinforced concrete slabs. The parametric study was carried out using the proposed ANN model to assess the effect of each input parameter on the PSS of two-way reinforced concrete slabs. Finally, a graphical user interface was developed to apply for practical design of PSS of two-way reinforced concrete slabs.

     

火灾下超高性能混凝土梁斜截面承载性能

为探究火灾下超高性能混凝土(ultra high performance concrete,UHPC)梁斜截面承载性能的退化与损伤演化规律,采用Abaqus建立16个UHPC梁的热-力耦合分析模型,选择剪跨比、载荷水平、配箍率、箍筋配置方式、纵筋配筋率等作为考察参数,通过与试验结果对比验证模型的正确性.火灾下UHPC梁斜截面承载性能削减严重,其破坏延性优于普通混凝土梁.载荷水平和箍筋配置方式是影响UHPC梁耐火极限的主要因素:随着载荷水平增大,耐火极限降低;配置箍筋可以提高试验梁在火灾下的延性,但降低其耐火极限.     

基于多目标进化算法的手机概念设计优化

针对手机设计领域应用计算机辅助设计存在的一些问题,以及如何处理相互冲突的多目标间的优化问题,深入地分析了概念设计过程中的创新思维和多目标优化的基本理论.在手机概念设计阶段同时考虑了用户要求.构件设计属性.设计成本和综合评价值等多种因素,将分布估计算法应该于求解手机集成的多目标优化问题,给出了具体的方法和步骤.实验结果表明,该方法可以提高设计的创新性,给设计人员提供有益的借鉴.     

Two-dimensional modeling of material failure in reinforced concrete by means of a continuum strong discontinuity approach

The paper presents a new methodology to model material failure, in two-dimensional reinforced concrete members, using the Continuum Strong Discontinuity Approach (CSDA). The mixture theory is used as the methodological approach to model reinforced concrete as a composite material, constituted by a plain concrete matrix reinforced with two embedded orthogonal long fiber bundles (rebars). Matrix failure is modeled on the basis of a continuum damage model, equipped with strain softening, whereas the rebars effects are modeled by means of phenomenological constitutive models devised to reproduce the axial non-linear behavior, as well as the bond-slip and dowel effects. The proposed methodology extends the fundamental ingredients of the standard Strong Discontinuity Approach, and the embedded discontinuity finite element formulations, in homogeneous materials, to matrix/fiber composite materials, as reinforced concrete. The specific aspects of the material failure modeling for those composites are also addressed. A number of available experimental tests are reproduced in order to illustrate the feasibility of the proposed methodology.     

A study on the quality improvement of robotic GMA welding process

With the advance of the robotic welding process, procedure optimisation that selects the welding procedure and predicts bead geometry that will be deposited has increased. A major concern involving procedure optimisation should define a welding procedure that can be shown to be the best with respect to some standard, and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation.

This paper represents a new algorithm to establish a mathematical model for predicting top-bead width through a neural network and multiple regression methods, to understand relationships between process parameters and top-bead width, and to predict process parameters on top-bead width in robotic gas metal arc (GMA) welding process. Using a series of robotic GMA welding, additional multi-pass butt welds were carried out in order to verify the performance of the multiple regression and neural network models as well as to select the most suitable model. The results show that not only the proposed models can predict the top-bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.     

Generation of strut-and-tie models by topology design using different material properties in tension and compression

The Strut-and-Tie Method is considered a basic tool for analysis and design of reinforced concrete structures and has been incorporated in different codes of practice such as: EC-2, BS 8110, ACI 318-08, EHE-08, etc. The stress trajectories or load path methods have been used to generate strut-and-tie models. However, the models produced by these methods are not unique, with the result depending on the intuition or expertise of the designer, specifically with regards to region D of the structure, where the load path distribution is non-linear. Topology optimization can offer new opportunities to eliminate the limitations of traditional methods. The aim of this work was to study the effect of using different mechanical properties for the steel reinforcement and for the concrete on the emerging topology of strut-and-tie models. The Isolines Topology Design (ITD) method was used for this research. Three examples are presented to show the effect of different mechanical properties used for the tensile (steel) and compressive (concrete) regions of the structure, the: (1) Single short corbel; (2) Deep beam with opening; and (3) Double-sided beam-to-column joint.     

A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping

Numerous real-world problems relating to ship design and shipping are characterised by combinatorially explosive alternatives as well as multiple conflicting objectives and are denoted as multi-objective combinatorial optimisation (MOCO) problems. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are multi-objective metaheuristics techniques, which fall in two categories: population-based search and trajectory-based search. This paper gives an overall view for the MOCO problems in ship design and shipping where considerable emphasis is put on evolutionary computation and the evaluation of trade-off solutions. A two-stage hybrid approach is proposed for solving a particular MOCO problem in ship design, subdivision arrangement of a ROPAX vessel. In the first stage, a multi-objective genetic algorithm method is employed to approximate the set of pareto-optimal solutions through an evolutionary optimisation process. In the subsequent stage, a higher-level decision-making approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker.     

Optimizing earthquake design of reinforced concrete bridge infrastructures based on evolutionary computation techniques

This paper presents a methodology for the earthquake design of reinforced concrete (RC) bridge infrastructures based on the application of multi-objective     

基于蚁群优化算法的纠删码存储系统数据更新方案

由于纠删码具备高可用性和高存储空间有效性的特点,采用纠删码为大规模分布式存储系统提供数据持久性已成为事实标准.然而,纠删码的密集型更新操作将导致大量的数据传输和I/O开销.如何减少数据传输量,优化现有网络资源的利用率,以提高纠删码的更新效率,成为纠删码存储系统面临的重要挑战.然而,在多重服务质量(quality of service,QoS)指标下,目前对纠删码更新效率的优化研究很少.针对此问题,提出一种基于蚁群优化算法的多数据节点更新方案(ant colony optimization algorithm based multiple data nodes update scheme,ACOUS),采用2阶段数据更新方式以优化多数据节点更新过程.具体而言,基于多目标蚁群优化更新路由算法(multi-objective ant colony optimization update routing algorithm,MACOU)所构建的多目标更新树,2阶段数据更新方式能有效地进行数据增量收集和校验增量分发.大量的实验结果表明,在典型的数据中心网络拓扑结构下,与TA-Update方案相比,所提方案能够在保证算法收敛的前提下,以可忽略的计算开销为代价,将更新时延降低26%~37%.     

Dynamic response simulation for reinforced concrete slabs

Present investigation comprises development of a new finite element numerical formulation for nonlinear transient dynamic analysis of reinforced concrete slab structures. Depending on many experimental data, new material constitutive relationships for concrete material have been formulated. A regression analysis of available experimental data in the SPSS-statistical program has been employed for formulating the proposed material finite element models, and the appropriateness of the models are confirmed through the histograms and measured indices of determination. Concrete slab structures were analyzed using eight-node serendipity degenerated plate elements. The constitutive models of the nonlinear materials are introduced to take into account the nonlinear stress–strain relationships of concrete. For studying the stress profile of the concrete slab through its thickness, a layered approach is adopted. Elastic perfectly plastic and strain hardening plasticity approaches have been employed to model the compressive behavior of concrete. Assumptions for strain rate effect were included in dynamic analysis by supposing the dynamic yield function as a function of the strain rate, in addition to be the total plastic strain. The yield condition is formulated in terms of the first two stress invariants. Geometrical nonlinearity was considered in analysis as a mathematical model based on the total lagrangian approach taking into account Von Karman assumptions. Implicit Newmark with corrector–predictor algorithm was used for time integration solution of the equation of the motion for slab structures. An incremental and iterative procedure is adopted to trace the entire response of the structure; a displacement convergence criterion is adopted in the present study. A computer program coded in FORTRAN has been developed and used for the dynamic analysis of reinforced concrete slabs. The numerical results show good agreement with other published studies’ results which include deflections.     

The prediction of ultimate pure bending moment of concrete-filled steel tubes by adaptive neuro-fuzzy inference system (ANFIS)

In this study, different modelling techniques such as multiple regression and adaptive neuro-fuzzy inference system (ANFIS) are used for predicting the ultimate pure bending of concrete-filled steel tubes (CFTs). The behaviour of CFT under pure bending is complex and highly nonlinear; therefore, forward modelling techniques can have considerable limitations in practical situations where fast and reliable solutions are required. Linear multiple regression (LMR), nonlinear multiple regression (NLMR) and ANFIS models were trained and checked using a large database that was constructed and populated from the literature. The database comprises 72 pure bending tests conducted on fabricated and cold-formed tubes filled with concrete. Out of 72 tests, 48 tests were conducted by the second author. Input variables for the models are the same with those used by existing codes and practices such as the tube thickness, tube outside diameter, steel yield strength, strength of concrete and shear span. A practical application example, showing the translation of constructed ANFIS model into design equations suitable for hand calculations, was provided. A sensitivity analysis was conducted on ANFIS and multiple regression models. It was found that the ANFIS model is more sensitive to change in input variables than LMR and NLMR models. Predictions from ANFIS models were compared with those obtained from LMR, NLMR, existing theory and a number of available codes and standards. The results indicate that the ANFIS model is capable of predicting the ultimate pure bending of CFT with a high degree of accuracy and outperforms other common methods.

     

Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation

Test suite minimisation techniques seek to reduce the effort required for regression testing by selecting a subset of test suites. In previous work, the problem has been considered as a single-objective optimisation problem. However, real world regression testing can be a complex process in which multiple testing criteria and constraints are involved. This paper presents the concept of Pareto efficiency for the test suite minimisation problem. The Pareto-efficient approach is inherently capable of dealing with multiple objectives, providing the decision maker with a group of solutions that are not dominated by each other. The paper illustrates the benefits of Pareto efficient multi-objective test suite minimisation with empirical studies of two and three objective formulations, in which multiple objectives such as coverage and past fault-detection history are considered. The paper utilises a hybrid, multi-objective genetic algorithm that combines the efficient approximation of the greedy approach with the capability of population based genetic algorithm to produce higher-quality Pareto fronts.     

Goal programming approach to solving network design problem with multiple objectives and demand uncertainty

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners’ NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure.     

Multi-objective design of post-tensioned concrete road bridges using artificial neural networks

In order to minimize the total expected cost, bridges have to be designed for safety and durability. This paper considers the cost, the safety, and the corrosion initiation time to design post-tensioned concrete box-girder road bridges. The deck is modeled by finite elements based on problem variables such as the cross-section geometry, the concrete grade, and the reinforcing and post-tensioning steel. An integrated multi-objective harmony search with artificial neural networks (ANNs) is proposed to reduce the high computing time required for the finite-element analysis and the increment in conflicting objectives. ANNs are trained through the results of previous bridge performance evaluations. Then, ANNs are used to evaluate the constraints and provide a direction towards the Pareto front. Finally, exact methods actualize and improve the Pareto set. The results show that the harmony search parameters should be progressively changed in a diversification-intensification strategy. This methodology provides trade-off solutions that are the cheapest ones for the safety and durability levels considered. Therefore, it is possible to choose an alternative that can be easily adjusted to each need.     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.     

Weight and cost oriented multi-objective optimisation of impact damage resistant stiffened composite panels

In this paper, a multi-objective optimisation procedure, based on the adoption of genetic algorithms, is presented. The optimal configuration with minimum weight and minimum cost of a damage resistant stiffened, composite panel with buckling constraints has been determined. The numerical procedure is based on an in-house optimisation code used in conjunction with the ANSYS FEM code. The presence of both continuous and high sensitivity discrete design variables, suggested for GA the adoption of a special bit-masking data structure able to increase the overall computational efficiency. Optimal configurations of the stiffened panel, are finally analysed and discussed focusing on the influence of the damage resistance constraint on the overall costs.     

Modeling of fresh concrete flow

This paper focuses on the modeling of fresh concrete flow. Concrete that is not properly casted or consolidated may have defects, such as air voids, honeycombs, and aggregate segregation. The modeling of fresh concrete flow can significantly contribute to the durability and strength of a structure and it is necessary for design optimization of casting procedure. The fresh concrete is considered as a non-Newtonian fluid. The Bingham model is used as constitutive model, with the yield stress and plastic viscosity as parameters. An interface-capturing approach is used to track the position of a free surface.