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Über dieses Buch

The chapters which appear in this volume are selected studies presented at the First International Conference on Engineering and Applied Sciences Optimization (OPT-i), Kos, Greece, 4-6 June 2014 and works written by friends, former colleagues and students of the late Professor M. G. Karlaftis; all in the area of optimization that he loved and published so much in himself. The subject areas represented here range from structural optimization, logistics, transportation, traffic and telecommunication networks to operational research, metaheuristics, multidisciplinary and multiphysics design optimization, etc.

This volume is dedicated to the life and the memory of Professor Matthew G. Karlaftis, who passed away a few hours before he was to give the opening speech at OPT-i. All contributions reflect the warmth and genuine friendship which he enjoyed from his associates and show how much his scientific contribution has been appreciated. He will be greatly missed and it is hoped that this volume will be received as a suitable memorial to his life and achievements.

Inhaltsverzeichnis

Frontmatter

Motorway Traffic Flow Optimisation in Presence of Vehicle Automation and Communication Systems

Abstract
This paper describes a novel approach for defining optimal strategies in motorway traffic flow control, considering that a portion of vehicles are equipped with Vehicle Automation and Communication Systems (VACS). An optimisation problem, formulated as a convex Quadratic Programming (QP) problem, is developed with the purpose of minimising traffic congestion. The proposed problem is based on a first-order macroscopic traffic flow model able to capture the lane changing and the capacity drop phenomena. An application example demonstrates the achievable improvements if the vehicles travelling on the motorway are influenced by the control actions computed as a solution of the optimisation problem.
Claudio Roncoli, Markos Papageorgiou, Ioannis Papamichail

Characterizing Urban Dynamics Using Large Scale Taxicab Data

Abstract
Understanding urban dynamics is of fundamental importance for the efficient operation and sustainable development of large cities. In this paper, we present a comprehensive study on characterizing urban dynamics using the large scale taxi data in New York City. The pick-up and drop-off locations are firstly analyzed separately to reveal the general trip pattern across the city and the existence of unbalanced trips. The inherent similarities among taxi trips are further investigated using the two-step clustering algorithm. It builds up the relationship among detached areas in terms of land use types, travel distances and departure time. Moreover, human mobility pattern are inferred from the taxi trip displacements and is found to follow two stages: an exponential distribution with short trips and a truncated power law distribution for longer trips. The result indicates that the taxi trip may not fully represent human mobility and is heavily affected by trip expenses and the urban form and geography.
Xinwu Qian, Xianyuan Zhan, Satish V. Ukkusuri

Holistic Calibration of Microscopic Traffic Flow Models: Methodology and Real World Application Studies

Abstract
This study proposes and applies a methodology to calibrate microscopic traffic flow simulation models. The proposed methodology has the capability to calibrate simultaneously all the calibration parameters as well as demand patterns for any type of network. Parameters considered include global and local as well as driver behaviour and vehicle performance parameters. Demand patterns, in terms of turning volumes, are included in the calibration framework. Multiple performance measures involving link counts and speeds are used to formulate and solve the proposed calibration problem. In addition, multiple time periods were considered. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used to search for the vector of the model’s parameters that minimizes the difference between actual and simulated network states. (Punzo V, Ciuffo B, Montanino M Transp Res Rec J Transp Res Board 2315(1):11–24 2012, Punzo et al. [1]) commented on the uncertainties present in many calibration methodologies. The motivation to consider simultaneously all model parameters is to reduce that uncertainties to a minimum, by leaving to the experience of the engineers as little parameter tuning as possible. The effects of changing the values of the parameters are taken into consideration to adjust them slightly and simultaneously. This results in a small number of evaluations of the objective function. Three networks were calibrated with excellent results. The first network was an arterial network with link counts and speeds used as performance measurements for calibration. The second network included a combination of freeway ramps and arterials, with link counts used as performance measurements. The third network was an arterial network, with time-dependent link counts and speed used as performance measurements. The experimental results illustrate the effectiveness and validity of this proposed methodology. The same set of calibration parameters was used in all experiments.
Alexander Paz, Victor Molano, Javier Sanchez-Medina

Optimizing Leisure Travel: Is BigData Ready to Improve the Joint Leisure Activities Efficiency?

Abstract
Over the past years we are witnessing an upsurge on the volume of travelers’ generated data. The upsurge of user-generated data from Smart Cards, Smart phones, personal navigators and social media has drawn the attention of the scientific community and new methods for utilizing such data in the areas of citizen-sensing, mobility understanding and travelers’ behavioral analysis have been developed and tested. Stepping ahead from the central problem of leveraging user-generated data for improving the scheduling of transport services, this survey paper tries to investigate the importance of big-data on improving the organizational efficiency of physical meetings among multiple travelers in urban environments. First, this work examines the state-of-the-art on capturing travelers’ patterns based on their data traces and the expected gains from leveraging user-generated data for optimizing leisure travel. Then, the problem of optimizing joint leisure travel is formulated and presented in an algorithmic form concluding to the suggestion of new research directions for future work.
K. Gkiotsalitis, A. Stathopoulos

Air Traffic Flow Management Data Mining and Analysis for In-flight Cost Optimization

Abstract
As the air traffic volume has increased significantly over the world, the great mass of traffic management data, named as Big Data, have also accumulated day by day. This factor presents more opportunities and also challenges as well in the study and development of Air Traffic Management (ATM). Usually, Decision Support Systems (DSS) are developed to improve the efficiency of ATM. The main problem for these systems is the data analysis to acquisition sufficient knowledge for the decision. This paper introduces the application of the methods of Data Mining to get the knowledge from air traffic Big Data in management processes. The proposed approach uses a Bayesian network for the data analysis to reduce the costs of flight delay. The process makes possible to adjust the flight plan such as the schedule of arrival at or departure from an airport and also checks the airspace control measurements considering weather conditions. An experimental study is conducted based on the flight scenarios between Los Angeles International Airport (LAX) and Miami International Airport (MIA).
Leonardo L. B. V. Cruciol, Li Weigang, John-Paul Clarke, Leihong Li

Simulation Optimization of Car-Following Models Using Flexible Techniques

Abstract
Car-following behavior is a key component of microscopic traffic simulation. Numerous models based on traffic flow theory have been developed for decades in order to represent the longitudinal interactions between vehicles as realistically as possible. Nowadays, there is a shift from conventional models to data-driven approaches. Data-driven methods are more flexible and allow the incorporation of additional information to the estimation of car-following models. On the other hand, conventional car-following models are founded on traffic flow theory, thus providing better insight into traffic behavior. The integration of data-driven methods in applications of intelligent transportation systems is an attractive perspective. Towards this direction, in this research an existing data-driven approach is further validated using another training dataset. Then, the methodology is enriched and an improved methodological framework is suggested for the optimization of car-following models. Machine learning techniques, such as classification, locally weighted regression (loess) and clustering, are innovatively integrated. In this chapter, validation of the proposed methods is demonstrated on data from two sources: (i) data collected from a sequence of instrumented vehicles in Naples, Italy, and (ii) data from the NGSIM project. In addition, a conventional car-following model, the Gipps’ model, is used as reference in order to monitor and evaluate the effectiveness of the proposed method. Based on the encouraging results, it is suggested that machine learning methods should be further investigated as they could ensure reliability and improvement in data driven estimation of car-following models.
Vasileia Papathanasopoulou, Constantinos Antoniou

Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

Abstract
With the overwhelming amount of transportation data being gathered worldwide, Intelligent Transportation Systems (ITS) are faced with several modeling challenges. New modeling paradigms based on Computational Intelligence (CI) that take advantage of the advent of big datasets have been systematically proposed in literature. Transportation optimization problems form a research field that has systematically benefited from CI. Nevertheless, when it comes to big data applications, research is still at an early stage. This work attempts to review the unique opportunities provided by ITS and big data and discuss the emerging approaches for transportation modeling. The literature dedicated to big data transportation applications related to CI and optimization is reviewed. Finally, the challenges and emerging opportunities for researchers working with such approaches are also acknowledged and discussed.
Eleni I. Vlahogianni

Nature-Inspired Algorithms: Success and Challenges

Abstract
The simplicity and flexibility of nature-inspired algorithms have made them very popular in optimization and computational intelligence. Here, we will discuss the key features of nature-inspired metaheuristic algorithms by analyzing their diversity and adaptation, exploration and exploitation, attractions and diffusion mechanisms. We also highlight the success and challenges concerning swarm intelligence, parameter tuning and parameter control as well as some open problems.
Xin-She Yang

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Abstract
Sustainable construction aims at reducing the environmental impact of buildings on human health and natural environment by efficiently using energy, resources and reducing waste and pollution. Building construction has the capacity to make a major contribution to a more sustainable future of our World because this industry is one of the largest contributors to global warming. The use of cold-formed steel framing in construction industry provides sustainable construction which requires less material to carry the same load compare to other materials and reduces amount of waste mimum design algorithms are developed for cold-formed steel frames made of thin-walled sections using the recent metaheuristic techniques. The algorithms considered are firefly, cuckoo search, artificial bee colony with levy flight, biogeography-based optimization and teaching-learning-based optimization algorithms. The design algorithms select the cold-formed thin-walled C-sections listed in AISI-LRFD (American Iron and Steel Institution, Load and Resistance Factor Design) in such a way that the design constraints specified by the code are satisfied and the weight of the steel frame is the minimum. A real size cold-formed steel building is optimized by using each of these algorithms and their performance in attaining the optimum designs is compared.
M. P. Saka, S. Carbas, I. Aydogdu, A. Akin, Z. W. Geem

Adaptive Switching of Variable-Fidelity Models in Population-Based Optimization

Abstract
This article presents a novel model management technique to be implemented in population-based heuristic optimization. This technique adaptively selects different computational models (both physics-based models and surrogate models) to be used during optimization, with the overall objective to result in optimal designs with high fidelity function estimates at a reasonable computational expense. For example, in optimizing an aircraft wing to obtain maximum lift-to-drag ratio, one can use low fidelity models such as given by the vortex lattice method, or a high fidelity finite volume model, or a surrogate model that substitutes the high-fidelity model. The information from these models with different levels of fidelity is integrated into the heuristic optimization process using the new adaptive model switching (AMS) technique. The model switching technique replaces the current model with the next higher fidelity model, when a stochastic switching criterion is met at a given iteration during the optimization process. The switching criterion is based on whether the uncertainty associated with the current model output dominates the latest improvement of the relative fitness function, where both the model output uncertainty and the function improvement (across the population) are expressed as probability distributions. For practical implementation, a measure of critical probability is used to regulate the degree of error that will be allowed, i.e., the fraction of instances where the improvement will be allowed to be lower than the model error, without having to change the model. In the absence of this critical probability, model management might become too conservative, leading to premature model-switching and thus higher computing expense. The proposed AMS-based optimization is applied to two design problems through Particle Swarm Optimization, which are: (i) Airfoil design, and (ii) Cantilever composite beam design. The application case studies of AMS illustrated: (i) the computational advantage of this method over purely high fidelity model-based optimization, and (ii) the accuracy advantage of this method over purely low fidelity model-based optimization.
Ali Mehmani, Souma Chowdhury, Weiyang Tong, Achille Messac

Genetic Algorithms for the Construction of $$2^{2}$$ 2 2 and $$2^{3}$$ 2 3 -Level Response Surface Designs

Abstract
Response surface methodology is widely used for developing, improving and optimizing processes in various fields. In this paper, we present a general algorithmic method for constructing \(2^q\)-level design matrices in order to explore and optimize response surfaces where the predictor variables are each at \(2^q\) equally spaced levels, by utilizing a genetic algorithm. We emphasize on various properties that arise from the implementation of the genetic algorithm, such as symmetries in different objective functions used and the representation of the \(2^q\) levels of the design with a \(q\)-bit Gray Code. We executed the genetic algorithm for \(q=2, 3\) and the produced four and eight-level designs achieve both properties of near-rotatability and estimation efficiency thus demonstrating the efficiency of the proposed heuristic.
Dimitris E. Simos

Reactive Power and Voltage Control Based on Mesh Adaptive Direct Search Algorithm

Abstract
This is a pioneer study that presents a new optimization algorithm called mesh adaptive direct search (MADS) to solve optimal steady-state performance of power systems. MADS is utilized to specify the optimal settings of control variables, i.e. transformer taps and generator voltages for optimal reactive power and voltage control of IEEE 30-bus system. Covariance matrix adaptation evolution strategy (CMAES) algorithm is utilized as a strong search strategy in the MADS technique to enhance its effectiveness. The results acquired by the hybrid search algorithm coupling MADS and CMAES, called MADS-CMAES, and the MADS algorithm itself without any search method are compared with multi-objective evolutionary and particle swarm optimization algorithms, demonstrating the superiority of MADS. The proposed MADS-based techniques are very robust against their parameters and changing the search space because of their inherent adaptive tuning.
Seyyed Soheil Sadat Hosseini, Amir H. Gandomi, Alireza Nemati, Seyed Hamidreza Sadat Hosseini

Optimal Placement of Hysteretic Dampers via Adaptive Sensitivity-Smoothing Algorithm

Abstract
Since hysteretic dampers have nonlinear restoring-force characteristics with sensitive plastic flow and input earthquake ground motions propagating random media are extremely random in time and frequency domains, the seismic response of a building structure with hysteretic dampers deviates greatly depending on the installed quantity and location of dampers. This characteristic could become a barrier and difficulty to the reliable formulation of optimal placement problems of such dampers. In order to overcome such difficulty, a new optimization method including a variable adaptive step length is proposed. The proposed method to solve the optimum design problem is a successive procedure which consists of two steps. The first step is a sensitivity analysis by using nonlinear time-history response analyses, and the second step is a modification of the set of damper quantities based upon the sensitivity analysis. Numerical examples are presented to demonstrate the effectiveness and validity of the proposed design method.
Yu Murakami, Katsuya Noshi, Kohei Fujita, Masaaki Tsuji, Izuru Takewaki

Design of Tuned Mass Dampers via Harmony Search for Different Optimization Objectives of Structures

Abstract
In this chapter, an optimization methodology for tuning of tuned mass dampers (TMDs) on seismic structures was presented for two different objectives such as reducing the displacement of first story and absolute acceleration of top story of the structure. A metaheuristic method; harmony search (HS) was employed for optimization according to the time history analyses of structure under several earthquake excitations. Harmony search inspires musical performances in order to find optimum design variables according to optimization objective. Step by step, the methodology of the optimization process is explained in the chapter. The method was applied to find an optimum TMD for a seven story shear building and the optimum results were compared for the two cases considering displacement objective and acceleration objective. According to the results, optimum TMDs for both objectives are effective on both displacements and accelerations. But for acceleration objective, a small benefit for accelerations can be seen although the optimum mass of TMD is very heavy according to displacement objective.
Sinan Melih Nigdeli, Gebrail Bekdaş

Tailoring Macroscale Response of Mechanical and Heat Transfer Systems by Topology Optimization of Microstructural Details

Abstract
The aim of this book chapter is to demonstrate a methodology for tailoring macroscale response by topology optimizing microstructural details. The microscale and macroscale response are completely coupled by treating the full model. The multiscale finite element method (MsFEM) for high-contrast material parameters is proposed to alleviate the high computational cost associated with solving the discrete systems arising during the topology optimization process. Problems within important engineering areas, heat transfer and linear elasticity, are considered for exemplifying the approach. It is demonstrated that it is important to account for the boundary effects to ensure prescribed behavior of the macrostructure. The obtained microstructures are designed for specific applications, in contrast to more traditional homogenization approaches where the microstructure is designed for specific material properties.
Joe Alexandersen, Boyan Stefanov Lazarov

Aerodynamic Shape Optimization Using “Turbulent” Adjoint And Robust Design in Fluid Mechanics

Abstract
This article presents adjoint methods for the computation of the first- and higher-order derivatives of objective functions \(F\) used in optimization problems governed by the Navier–Stokes equations in aero/hydrodynamics. The first part of the chapter summarizes developments and findings related to the application of the continuous adjoint method to turbulence models, such as the Spalart-Allmaras and k-\(\varepsilon \) ones, in either their low- or high-Reynolds number (with wall functions) variants. Differentiating the turbulence model, over and above to the differentiation of the mean–flow equations, leads to the computation of the exact gradient of \(F\), by overcoming the frequently made assumption of neglecting turbulence variations. The second part deals with higher-order sensitivity analysis based on the combined use of the adjoint approach and the direct differentiation of the governing PDEs. In robust design problems, the so-called second-moment approach requires the computation of second-order derivatives of \(F\) with respect to (w.r.t.) the environmental or uncertain variables; in addition, any gradient-based optimization algorithm requires third-order mixed derivatives w.r.t. both the environmental and design variables; various ways to compute them are discussed and the most efficient is adopted. The equivalence of the continuous and discrete adjoint for this type of computations is demonstrated. In the last part, some other relevant recent achievements regarding the adjoint approach are discussed. Finally, using the aforementioned adjoint methods, industrial geometries are optimized. The application domain includes both incompressible or compressible fluid flow applications.
Kyriakos C. Giannakoglou, Dimitrios I. Papadimitriou, Evangelos M. Papoutsis-Kiachagias, Ioannis S. Kavvadias

Hierarchical Topology Optimization for Bone Tissue Scaffold: Preliminary Results on the Design of a Fracture Fixation Plate

Abstract
A porous material can be designed to promote tissue regeneration as well as satisfy mechanical and biological requirements. The porous microarchitecture can be specifically tailored to locally match the specific properties of the host tissue resulting in a biologically fixed implant. A 2D hierarchical topology optimization scheme is presented here to design a cellular scaffold that optimally reconciles bone resorption and permeability, two antagonist objectives of bone tissue scaffolds. The implant is tailored to reproduce the variable stiffness properties of the surrounding bone while maximizing its permeability for bone ingrowth. The procedure integrates multi-objective optimization with multi-scale topology optimization. In particular, the material layout is sequentially optimized at two length scales: (1) the property distribution varying throughout the implant body, and (2) the topology of each pore of the scaffold. In the first stage, an optimal material distribution is obtained to generate a stiffness match between implant and bone tissue. In the second stage, the optimal relative density distribution is used to interpolate target material properties at each location of the implant domain. Target matching topology optimization is used to obtain unit cells with desired stiffness and maximum permeability throughout the implant. The procedure currently developed in 2D can be extended to produce clinically relevant 3D implant models. As a case study, a 2D bone fracture fixation plate under in-plane load is optimized at both the implant and cellular material level. While the preliminary results presented here need further refinement, such as on the filtering method and the calculation of permeability, the paper contributes to the development of a method to design engineered scaffolds that are both mechanically optimal and conducive to bone tissue regeneration.
Emily Gogarty, Damiano Pasini

Boundary Constraint Handling Affection on Slope Stability Analysis

Abstract
In an engineering optimization problem such as soil slope problem, each design variable has permissible solution domain. Therefore, efficiency of an optimization algorithm may be affected by the method used for keeping the solutions within the defined boundaries or boundary constraint handling method. Despite importance of selecting constraint handling approach, there aren’t adequate studies in this field. Heterogeneous slope stability optimization in the presence of a band of weak soil layer is considered as a complex geotechnical problem that requires satisfying boundary constraints. Evolutionary boundary constraint handling is one of the recently proposed methods that is very easy to implement and very effective. The present study intended to improve the optimization results by means of evolutionary boundary constraint handling scheme on slope stability optimization problem. In the current chapter five benchmark problems are analyzed using absorbing and evolutionary boundary constraint handling schemes and their results are compared to check the validity of this method. Based on achieved results optimization algorithm performance is improved by using the proposed boundary constraint handling method.
Amir H. Gandomi, Ali R. Kashani, Mehdi Mousavi

Blackbox Optimization in Engineering Design: Adaptive Statistical Surrogates and Direct Search Algorithms

Abstract
Simulation-based design optimization relies on computational models to evaluate objective and constraint functions. Typical challenges of solving simulation-based design optimization problems include unavailable gradients or unreliable approximations thereof, excessive computational cost, numerical noise, multi-modality and even the models’ failure to return a value. It has become common to use the term “blackbox” for a computational model that features any of these characteristics and/or is inaccessible by the design engineer (i.e., cannot be modified directly to address these issues). A possible remedy for dealing with blackboxes is to use surrogate-based derivative-free optimization methods. However, this has to be done carefully using appropriate formulations and algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary design optimization problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.
Bastien Talgorn, Le Digabel Sébastien, Michael Kokkolaras

Life Cycle Analysis and Optimization of a Steel Building

Abstract
The present study seeks to couple the problem of the structural optimization of building frames, with that of the optimization of design options for their energy efficiency. The objective function is a cost function that takes into account both the structural cost and energy performance along the whole life of the building. Consequently, the following design parameters are involved: insulation thickness, wall and window insulation profile, window sizes, heating and air conditioning system sizing, sizing of steel cross-sections, as well as parameters related to the life cycle of the building. Modeling is based on acceptable from national and European regulations procedures. Optimization is solved using evolutionary algorithms. The optimization problem is implemented on a steel office building (\(10\times 15\) m), in Chania, Crete, at the south part of Greece. This is a first attempt to combine Life Cycle Cost and Optimization with classical Structural Optimization for steel structures. Depending on the requirements from the users of the building further evaluation using building energy management system (BEMS) for the intelligent operation and management of heating, ventilation and air-conditioning (HVAC) may be performed.
G. K. Bekas, D. N. Kaziolas, G. E. Stavroulakis

Optimization of Reinforced Concrete Columns Subjected to Uniaxial Loading

Abstract
The distance from extreme compression fiber to neutral axis (c) is depended to combinations of axial load and flexural moment capacities of reinforced concrete (RC) columns. Since c is depended to different internal forces, the value of c cannot be found without assuming the final design. Thus, it can be iteratively searched in order to find the flexural moment capacity of columns under an axial loading. By using the presented method, the solution with the minimum cost ensuring maximum flexural moment and axial load is found. A random search technique is explained in this chapter for optimum design of uniaxial RC columns with minimum cost. In optimization, design of RC columns is done by considering the design rules described in ACI 318- Building Code Requirements for Structural Concrete. The random search technique (RST) for optimization of RC uniaxial columns is effective on finding optimum cross-sections and reinforcement design with minimum cost.
Gebrail Bekdaş, Sinan Melih Nigdeli

The Effect of Stakeholder Interactions on Design Decisions

Abstract
The success of an engineering project typically involves multiple stakeholders beyond the designer alone, such as customers, regulators, or design competitors. Each of these stakeholders is a dynamic decision maker, optimizing their decisions in order to maximize their own profits. However, traditional design optimization often does not account for these interactions, or relies on approximations of stakeholder preferences. Utilizing game theory, we propose a framework for understanding the types of interactions that may take place and their effect on the design optimization formulation. These effects can be considered as an economic uncertainty that arises due to limited information about interactions between stakeholders. This framework is demonstrated for a simple example of interactions between an aircraft designer and an airline. It is found that even in the case of very simple interactions, changes in market conditions can have a significant impact on stakeholder behaviors and therefore on the optimal design. This suggests that these interactions should be given consideration during design optimization.
Garrett Waycaster, Christian Bes, Volodymyr Bilotkach, Christian Gogu, Raphael Haftka, Nam-Ho Kim

A Fixed Point Approach to Bi-level Multi-objective Problems

Abstract
The present note aims at introducing a new approach for handling bi-level multi-objective problems. The advantage consists in the fact that, for solving the upper level, it does not require to know explicitly the lower level. Here, the linear case is fully treated. Hints are given on how to extent it to the important class of the cono-functions, which contains that of the convex functions, and is one of the few extensions of convex functions which are numerically viable. The final section gives suggestions for further research in the field.
Carla Antoni, Franco Giannessi

Reliability-Based Shape Design Optimization of Structures Subjected to Fatigue

Abstract
Fatigue has been played a key role into the design process of structures, since many failures of them are attributed to repeated loading and unloading conditions. Crack growth due to fatigue, represents a critical issue for the integrity and resistance of structures and several numerical methods mainly based on fracture mechanics have been proposed in order to address this issue. Apart from loading, the shape of the structures is directly attributed to their service life. In this study, the extended finite element is integrated into a shape design optimization framework aiming to improve the service life of structural components subject to fatigue. The relation between the geometry of the structural component with the service life is also examined. This investigation is extended into a probabilistic design framework considering both material properties and crack tip initialization as random variables. The applicability and potential of the formulations presented are demonstrated with a characteristic numerical example. It is shown that with proper shape changes, the service life of structural component can be enhanced significantly. Comparisons with optimized shapes found for targeted service life are also addressed, while the choice of initial imperfection position and orientation was found to have a significant effect on the optimal shapes.
Manolis Georgioudakis, Nikos D. Lagaros, Manolis Papadrakakis

A Stress-Test of Alternative Formulations and Algorithmic Configurations for the Binary Combinatorial Optimization of Bridges Rehabilitation Selection

Abstract
Optimal surface transport asset management is a major concern with multiple economic and operational implications developed in various infrastructure areas. Although relevant ‘mature’ analytical frameworks have been proposed and developed, the problem setup and the algorithmic choices are still issues requiring thorough and detailed investigation. In this chapter, an optimal budget allocation framework is developed and stress-tested for the optimal scheduling of a bridges upgrading program. A suitable test case is developed for performing in-depth analysis that takes into consideration the most important features involved in such scheduling problems, while alternative formulations are also presented and discussed. The proposed frameworks are applied on a real large-scale dataset from the highway system of US, able to provide an adequate test-bed for investigating the optimal upgrade problem. The paper aims in the investigation of the effects that alterations of the problem setup, but also the effects that algorithmic configurations are introducing, when addressing real-world applications. The binary/selection problem is handled with a suitably coded Branch-and-Bound (BaB) algorithm, which is regarded as a robust and fast heuristic for such optimization problems. BaB is tested in alternative standard and extreme configurations, offering insights on its performance. Interestingly enough, although the continuous relaxation introduced by the BaB enables fast convergence, the NP-hard problem’s nature should be cautiously taken into consideration. The results are discussed in order to provide insights of applying the proposed framework in realistic infrastructure upgrading schemes.
Dimos C. Charmpis, Loukas Dimitriou
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