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Recent natural or manmade hazards highlighted the pressing need for allocating enough financial resources for the enhancement of the resilience of infrastructure in our society. In this regard, bridges play a pivotal role in transportation networks, where any reduction in their performance can influence the functionality of the whole network. Bridge retrofitting is a common strategy for enhancing transportation network resilience. Composite reinforcement with different thicknesses and concrete seat extenders with different widths have been used to retrofit an existing curved bridge in Iran. A novel hybrid algorithm called NSGAII-SA was proposed and compared against a simple optimisation strategy, a traditional genetic algorithm solver (NSGA-II), and pure simulated annealing (SA). These optimisation strategies identify the optimal retrofit scenarios set in terms of maximum resilience index, minimum recovery time, and minimum retrofitting cost. For this problem, NSGAII-SA outperforms the traditional NSGA-II. The comparison shows that for this particular application, SA has the best overall performance. However, the proposed NSGAII-SA algorithm has satisfactory performance too, it is a clear improvement over NSGAII and it has the potential to be very useful and better suited than SA for problems with large design spaces and many local minima. [ABSTRACT FROM AUTHOR]

The integration of building seismic losses and environmental impacts is a key step toward the improvement of building design approaches accounting for resilience and sustainability in parallel. A decision-making framework including the life-cycle energy assessment and seismic loss estimation is proposed in this study in order to investigate the trade-offs between buildings' environmental sustainability and reducing seismic loss. The framework includes a Pareto front analysis to reach a balance among the criteria. Building operational energy, embodied energy, seismic repair cost, and injuries were the performance metrics considered. The framework was applied to three reinforced concrete building designs including low- to high-rise buildings along with six exterior wall assemblies and four Window-to-Wall-Ratios. The contradictory trend among the performance metrics underlines the importance of merging these concepts for the development of the current building design methodologies. The hypervolume indicator was computed for each building's Pareto front set to evaluate the quality of Pareto front design solutions. The results showed that compared to the 20-story building, the 2- and 12-story buildings have higher number of Pareto solution points and the solutions are of higher qualities in terms of diversity and proximity to the true Pareto front based on the hypervolume indicator solutions. [ABSTRACT FROM AUTHOR]

Facing the contradiction between increasing maintenance demand and limited financial, as well as smaller social and environmental impacts, the optimisation of bridge maintenance strategies should be carried out. In this paper, the assessment model of four objectives, including the safety performance, economic, social and environmental impacts, in sustainability criteria are proposed. Moreover, a systematic optimisation framework of maintenance strategy of deteriorating bridges considering sustainability criteria and a novel decision-making method are developed, based on fuzzy analytic hierarchy process (FAHP) and a decision-making algorithm based on linear programming techniques for multidimensional analysis of preference (LINMAP). The case analysis results verify the effectiveness and superiority of the previous optimisation framework, and also show that the final optimal strategy obtained by the proposed decision-making algorithm can reflect the will of experts well. Compare with the essential maintenance, the implementation of preventive maintenance is more conducive to realise the sustainable development of the deteriorating bridges. The sensitivity analysis results show that the monetary discount rate has a greater impact on the life cycle maintenance cost, which is mainly controlled by the total social cost, while the direct maintenance cost only accounts for a small part. [ABSTRACT FROM AUTHOR]

Composite beams comprising concrete slabs supported by steel beams typically constitute more than 70% of the total volume of the structure of an office building. Little, if any, research has been conducted regarding the design and construction of a floor system that successfully accounts for eco-friendliness. To remedy this, an optimal sustainable design model for composite beams (OSDMCB) has been developed. The proposed OSDMCB was used to analyse the relationship between CO2 emissions and costs required for the construction of concrete slabs supported by steel beams composed of four different materials: concrete, steel frame, rebar, and a shear connector. To verify the performance of the OSDMCB, it was applied to the design and construction of composite beams in a 30-story office building. When both costs and CO2 emissions are simultaneously minimised, the composite beam of the OSDMCB, compared with a composite beam without the OSDMCB, reduces the costs and CO2 emissions by 6.23% and 4.07%, respectively. [ABSTRACT FROM AUTHOR]

Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers. [ABSTRACT FROM AUTHOR]

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints. [ABSTRACT FROM AUTHOR]

Addressing the multidimensional challenges involved in advancing the sustainability of pavement systems requires the development of optimisation-based decision support system (DSS) for pavement management with the capability to identify optimally sustainable pavement maintenance and rehabilitations (M&R) strategies. The main objective of this research work is to develop a multi-objective optimisation framework that hosts a comprehensive and integrated pavement life cycle costs-life cycle assessment model that covers the pavement's whole life cycle, from the extraction and production of materials to construction and maintenance, transportation of materials, work-zone traffic management, usage and end-of-life. The capability of the proposed DSS is analysed in a case study aiming at investigating, from a full life cycle perspective, the extent to which a number of pavement engineering solutions are efficient in improving the environmental and economic aspects of pavement sustainability, when applied in the management of a road pavement section. Multiple bi-objective optimisation analyses considering accordingly agency costs, user costs and greenhouse gas emissions were conducted based on a multi-objective genetic algorithm. Pareto fronts were obtained for each analysis, originating a set of non-dominated maintenance and rehabilitation solutions. Posteriorly, a multi-criteria decision analysis method was used to find the best compromise solution for pavement management. [ABSTRACT FROM AUTHOR]

The inspection and maintenance plans to ensure the structural safety and extend the service life of deteriorating structures can be established effectively through an optimisation process. When several objectives are required for inspection and maintenance strategies, a multi-objective optimisation process needs to be used in order to consider all objectives simultaneously and to rationally select a well-balanced solution. However, as the number of objectives increases, additional computational efforts are required to obtain the Pareto solutions, for decision-making to select well-balanced solutions, and for visualisation of the solutions. This paper presents a novel approach to multi-objective optimisation process of probabilistic service life management with four objectives: minimising the damage detection delay, minimising the probability of failure, maximising the extended service life and minimising the expected total life-cycle cost. With these four objectives, the single, bi-, tri- and quad-objective optimisation processes are investigated using the weighted sum method and genetic algorithms. The objective reduction approach with the Pareto optimal solutions is applied to estimate the degree of conflict among the objectives, and to identify the redundant objectives and minimum essential objective set. As a result, the efficiency in decision-making and visualisation for service life management can be improved by removing the redundant objectives. [ABSTRACT FROM AUTHOR]

Thedecayingstate of our infrastructure paired with rising emphasis on sustainable engineering suggests the need for upgrades which are selected based on their ability to provide adequate performance under natural hazards and minimise negative impacts on the three pillars of sustainability: society, environment and economy. This paper poses a framework based on a multi-objective genetic algorithm to help identify optimal retrofit and repair combinations which ensure public safety while minimising lifetime environmental, economic and social performance measures of sustainability for infrastructure exposed to natural hazards. Assessment of the case-study application results reveals the relationship between life-cycle environmental, economic and social indicators of sustainability for a bridge subject to earthquakes. The framework is anticipated to help guide the selection of retrofit and repair combinations by providing a set of ‘near-optimal’ non-dominated solutions, which enhance sustainability while ensuring public safety and mitigating or repairing damage from natural disasters. [ABSTRACT FROM PUBLISHER]

A new multi-objective decision support system (MODSS) is developed for rehabilitation planning of public infrastructures. The method is generic and provides decision-makers a set of optimal rehabilitation tradeoffs over a desired analysis period. Two main objective functions are handled simultaneously, namely cost and performance, in addition to a set of bounding constraints. The method is based on a newfitness-orientedtechnique where problem knowledge is taken into account. In order to analyse cost and performance together, a normalisation technique of both objectives is achieved through an innovative time-value concept for both cost and condition states. The proposed method is based on life-cycle costing (LCC) methodology using a dynamic Markov chain to represent the deterioration mechanism and genetic algorithm is used to find the optimal rehabilitation profile. A case study is presented with a comparison between the traditional Markov decision process (MDP) and the newly developed method. The MODSS results in a lower LCC and is found practical in providing a complete maintenance and rehabilitation plan over a required study period. It is proven that the developed multi-objective optimisation is an effective tool in analysing real-life situations involving conflicting goals. Also, weighted sum method could be easily implemented and its outcome is sufficient given that many external factors might alter the decision-makers choice irrespectively of the optimisation method that is used. Furthermore, genetic algorithm is proven useful in the optimisation process in overcoming the computational difficulties associated with large combinatorial problems. The new method is beneficial to researchers and practitioners as it provides a major step towards a broad infrastructure management system. [ABSTRACT FROM PUBLISHER]

This article presents the implementation of a calibration procedure for a finite element (FE) model of a state highway bridge using sensory data measured on the bridge. The objective is to modify the high-fidelity FE model of the bridge so that its dynamic behaviour matches, as closely as possible, that of the bridge under analysis. The bridge under investigation is a steel–concrete composite bridge that is instrumented with a wireless monitoring system to collect its vibration response under ambient vibrations. A detailed three-dimensional FE model of the bridge was developed to represent the bridge as realistically as possible. The detailed modelling can minimise the amount of uncertainty in the model and the number of parameters that require updating. A multi-variable sensitivity-based objective function is used to minimise the error between the experimentally measured and the FE-computed modal characteristics. An iterative optimisation approach has been undertaken to find the optimum structural parameters of the FE model that minimise the selected objective function. It is shown that developing a high-fidelity FE model may help simplify the model calibration process for a large structure, which is contrary to conventional thinking on the subject. [ABSTRACT FROM AUTHOR]

Economic efficiency is an important factor in seismic rehabilitation of road infrastructures. An inventory of structures is screened to identify seismically deficient parts and prioritise them in the order of need for rehabilitation purposes. In most cases, the financial resources for road infrastructure rehabilitation projects are limited. Therefore, there is a need to efficiently allocate resources to various projects. This article presents a multi-strategy decision support system (DSS) for seismic rehabilitation budget allocation across existing road infrastructures. This DSS employs a multi-criteria assessment module that takes into account different criteria to estimate the financial needs for rehabilitation and to establish an allocation methodology based on the available budget in a fiscal year. To select the optimal package of projects, a genetic algorithm (GA) optimisation module is developed. Multi-objective decision-making is conducted under a specific decision strategy (DS) by solving a (0–1) Knapsack problem. To demonstrate the applicability of the GA-based approach, a hypothetical decision-making problem is presented. The results reveal that the optimal package is more sensitive to the available budget and DS than to the weights of criteria and project scores. Using this system, managers can compare their decisions for different strategies and significantly improve management efficiency. [ABSTRACT FROM AUTHOR]

A new preference-based method is presented to determine optimal schedule of maintenance actions for deteriorating bridges. The maintenance planning of deteriorating bridges is formulated as a multi-objective optimisation problem involving the maintenance cost as well as the condition grades of the bridge deck, girder and pier. The maintenance cost corresponds to life-cycle maintenance cost that is defined as the sum of the total expenses caused by the maintenance interventions during the life span of the bridge. The condition grades of the bridge components are characterised by time-dependent performance profiles that have been constructed by statistical regression with the actual condition grade data of bridges in Korea. To effectively address the multi-objective optimisation problem, a genetic algorithm is adopted as a numerical searching technique of multiple optimal solutions. As for the explored optimal solutions, a preference-based decision making process is then introduced to select reasonable maintenance scenario that most fits the designer's preference while balancing the mutually conflicting design objectives. The effectiveness of the proposed method is verified numerically on a typical pre-stressed concrete girder bridge. The numerical results demonstrate that the optimal maintenance scenario determined by the proposed method can be a well-balanced maintenance strategy in terms of cost and bridge performances. [ABSTRACT FROM AUTHOR]

Our knowledge to model, analyse, design, maintain, monitor, manage, predict and optimise the life-cycle performance of structures and infrastructures under uncertainty is continually growing. However, in many countries, including the United States, the civil infrastructure is no longer within desired levels of performance and safety. Decisions regarding civil infrastructure systems should be supported by an integrated reliability-based life-cycle multi-objective optimisation framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this paper is to highlight recent accomplishments in the life-cycle performance assessment, maintenance, monitoring, management and optimisation of structural systems under uncertainty. Challenges are also identified. [ABSTRACT FROM AUTHOR]

The life-cycle cost (LCC) concepts and methods have impacted remarkably on the field of civil infrastructure. In this paper, LCC design concepts and methods are discussed with emphasis on bridges and road networks. The relationships among several performance measures are discussed. An attempt is made to provide rational balances of these measures by using a multi-objective genetic algorithm. Furthermore, LCC is evaluated focusing on the effects of earthquakes. At first, LCC analysis is formulated to consider the socio-economic effects due to the collapse of structures as well as the minimisation of lifetime maintenance cost. A stochastic model of structural response under earthquake effects is proposed. Then, the probability of failure of an individual bridge due to the earthquake excitation is calculated based on the reliability theory. In addition, LCC evaluations are performed not only for a single bridge but also for groups of bridges forming road networks. [ABSTRACT FROM AUTHOR]