Fitness-oriented multi-objective optimisation for infrastructures rehabilitations

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 new fitness-oriented technique 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.     

Empirical Markovian-based models for rehabilitated pavement performance used in a life cycle analysis approach

Two empirical Markovian-based models are presented in this paper to predict the transition probabilities associated with rehabilitated pavement. The first model predicts the staged-homogenous transition probabilities as required by the staged-homogenous Markov model. The second model predicts the non-homogenous transition probabilities as applicable to the non-homogenous Markov model. In both the models, the deterioration transition probabilities are predicted as a function of the corresponding values associated with original pavement and two adjustment factors reflecting the impacts of increased traffic load applications and decreased pavement strength. The predicted transition probabilities are used to estimate the future distress ratings required for developing the corresponding life cycle performance curve. The life cycle performance/cost ratio is used to evaluate the cost-effectiveness of potential long-term M&R plans. The life cycle performance is defined as the area falling under the life cycle curve. The life cycle cost is estimated to include initial construction cost, routine maintenance cost, major rehabilitation cost, and added user cost due to work zone. Two proposed cost models are used in the case study for estimating routine maintenance and added user costs. The case study indicates that the proposed empirical Markovian-based models have provided reasonable estimates of the transition probabilities as reflected by the corresponding life cycle performance curves.     

Algorithms for bottom-up maintenance optimisation for heterogeneous infrastructure systems

This paper presents a methodology for maintenance optimisation for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials, and deterioration processes. We present a bottom-up approach: facility-level optimal maintenance policies are first found; these policies are then combined with budget constraints in the system-level optimisation. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimisation. In the second step, the problem is formulated as a constrained combinatorial optimisation problem, where the best combination of facility-level optimal and near-optimal solutions is identified. Two heuristics, pattern search heuristic (PSH) and evolutionary algorithm (EA), are adopted to solve the combinatorial optimisation problem. Their performance is evaluated using a hypothetical system of pavement sections. Comparison result with real optimal solutions for 20 facilities showed that both algorithms give near-optimal solutions (within less than 0.1% difference from the optimal solution) in 978 (PSH) and 966 (EA) cases out of 1000 executions. The EA performs better in terms of processing time than the PSH. Numerical experiments show the potential of the proposed algorithms to solve the maintenance optimisation problem for realistic heterogeneous systems.     

A multi-objective approach for detecting and responding to accidental and intentional contamination events in water distribution systems

The protection against contamination events in water distribution systems involves two distinct phases: detection of the presence of a contaminant and implementation of actions to isolate and/or expel it rapidly. The problem of detection is confronted by installing a series of monitoring stations, strategically placed across the distribution system and consisting of sensors to detect the presence of contaminants. The actions to be implemented may include operations on distribution system devices (valves and hydrants) or injection of reagents to eliminate the contaminant, or simply alert users. The procedure proposed here attempts to address the problems related to the two phases by means of two consecutive optimisation processes, both of them performed off-line and assuming a specific 24-hour water demand sequence in each network node, whereas the accidental/intentional injection of contaminant can occur in any node and at any hour of the day. With reference to this vast range of possible injection scenarios, the first multi-objective optimisation process defines the position of a pre-selected number n_s of sensors across the distribution system in order to minimise the expected percentage of undetected contamination events and the expected volume of contaminated water consumed up to the beginning of the response operations following detection. A single configuration of stations is then selected from the Pareto front produced by this optimisation process (‘knee point’ of the Pareto front). At the end of this first optimisation process and with reference to the selected set of sensors, a potentially contaminated area in the network is associated to each sensor for each sub-period of the day. The second multi-objective optimisation process is then aimed to identify, with reference to each station and sub-period, and thus inside the corresponding potentially contaminated area, the hydrant-opening and valve-closing operations to be carried out in order to minimise both the number of operations and the expected volume of contaminated water consumed between the beginning of the response operations and the disappearance of the contaminant, assuming the availability of an unlimited number of response teams. Once these devices have been identified (‘knee point’ of the Pareto front relevant to the second optimisation process), an a posteriori analysis is performed to determine the sequence in which they should be activated based on the number of response teams actually available. In these optimisation processes, a hydraulic and quality simulator (EPANET) is linked to a multi-objective genetic algorithm (NSGA-II) in order to compute the value of the objective functions of the problem across different contamination scenarios. The results obtained applying the procedure to a real and complex water distribution system have shown it to be a robust and effective method for reducing the impact on the population.     

A life cycle assessment of in-place recycling and conventional pavement construction and maintenance practices

The application of in-place recycling techniques has emerged as a practical and effective way to enhance the sustainability of agency pavement management decisions for asphalt-surfaced pavements. However, the potential environmental benefits resulting from applying in-place recycling techniques have not been fully documented in the literature. This paper presents a comprehensive pavement life cycle assessment (LCA) model that extends the typical pavement LCA's system boundaries to include the environmental impacts resulting from the usage phase and the production of the energy sources. The results of the application of the pavement LCA model to a specific highway rehabilitation project in the state of Virginia showed that in-place recycling practices and an effective control of the pavement roughness can improve significantly the life cycle environmental performance of a pavement system.     

A simulation optimisation tool for planning of low-income housing projects

Construction of low-income housing projects is a recurring process and is associated with uncertainties that arise from the unavailability of resources. This paper presents a case study that discusses how computer simulation and optimisation are used to aid government agencies and/or contractors in planning of such projects. It illustrates the optimisation of project objectives, taking into consideration the interaction amongst involved resources. As such, total duration and the associated total costs, including direct and indirect costs, can be estimated and optimised. One Youth Habitation project that is executed in 6th of October City in Egypt is analysed in a step-by-step procedure to demonstrate the capability of proposed computer simulation and optimisation prototype (named LIHouse{_}Sim) in the modelling construction of low-income housing projects using bearing block walls with hollow core technique. The presented tool proves its practicality to contractors in estimating the time and costs of the recurring process of low-income housing construction, considering complex interdependencies between construction resources and the uncertainties associated with construction activities. The LIHouse{_}Sim prototype is used to perform a wide analysis for the alternative of the effective optimisation criteria in the bearing block walls/hollow core technique and for the genetic algorithm optimisation approach elements.     

Enhancing life cycle cost analysis with a novel cost classification framework for pavement rehabilitation projects

Life cycle cost analysis (LCCA) procedures have been used over the past decades to justify the choice of one pavement design alternative over the others. However, many ambiguities associated with the life cycle cost input values, such as the discount rate and future cost estimates have questioned the credibility of the analysis results. Another unrecognized source of errors in pavement LCCA is the misunderstanding of pavement treatment costs when historical costs are typically used for estimating those costs. The historical costs of pavement rehabilitation projects typically include a significant amount of non-pavement-related costs, which may result in a wrong LCCA if not treated appropriately. This paper addresses this specific point of error and proposes a solution to eliminating this error by using a novel cost classification framework that successfully differentiates mainline roadway costs from non-pavement cost items. A case study using Monte Carlo simulation is conducted to evaluate the probabilistic LCCA results. The results of the case study indicate that the conventional approach of using total rehabilitation project costs in LCCA may even lead to a wrong investment decision. The findings of this study will help practitioners and researchers better understand the nature of pavement rehabilitation project cost distributions.     

基于WebGIS的县级公路路面养护管理系统分析

在分析目前县级公路路面建设与养护问题的基础上,以ArcGISServer为开发平台,重点对基于WebGIS的县级公路路面养护与管理系统的结构、技术方案和总体功能进行了设计与说明,并指出基于WebGIS的路面养护管理系统是该技术未来发展的必然方向。     

Extension of dynamic programming models for management optimisation from single structure to multi-structures level

The aim of this article is the management optimisation (inspection, maintenance and rehabilitation (IM&R)) of a group of structures. It is supposed that the optimisation is constrained by limited available budget at the beginning of each time period during the entire time horizon thus creating an economical dependence between the decisions related to each of the structures. A Lagrangian relaxation technique is used for the extension of existing dynamic programming methods from single structure to multi-structures level. The methodology is illustrated by using a Generalised Partially Observable Markov Decision Process having a decision tree composed of a sequence of two decisions at the beginning of each time period, namely an inspection decision followed by a maintenance action decision. A numerical example concerning the optimisation of IM&R of 16 different bridges is presented.     

Multi-strategy budget allocation decision support system for seismic rehabilitation of road infrastructure

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.     

Optimal maintenance strategies for bridge networks using the supply and demand approach

This paper presents a probability-based approach for optimising the management of bridge networks. Most of the Bridge Management Systems are focused on condition features to ensure a minimum safety level for each individual bridge. Their location on the road network, the consequences of inadequate service due to maintenance actions are therefore not taken into consideration. These multiple criteria should be considered when scheduling maintenance activities. To overcome these limitations, a probabilistic supply and demand strategy is proposed for determining the optimal maintenance planning for each interconnected bridge. The problem is solved with genetic algorithms. One objective function is first introduced, corresponding to the summation of all the maintenance, failure, and user costs. Then, two conflicting objective functions are considered, the total user costs and the maintenance and rehabilitation costs. Safety and serviceability aspects are taken into account in the methodology and the theoretical and numerical developments are applied on a part of the French national network.     

Multi-objective and probabilistic decision-making approaches to sustainable design and management of highway bridge decks

The aging and deterioration of highway bridges and the new requirements for sustainable infrastructures and communities require innovative approaches for their management that can achieve an adequate balance between social, economic and environmental sustainability. This paper presents a multi-objective decision-making approach for the sustainable design and management of highway bridge decks, which can consider several and conflicting objectives, such as the minimisation of owner's costs, users costs, and environmental impacts and uses goal setting and compromise programming to determine the satisficing and compromise solutions that yield the best trade-off between all competing objectives. The proposed approach is based on robust reliability-based mechanistic models of the deterioration and service life of reinforced concrete bridge decks, which include diffusion models for the prediction of chloride ingress into concrete and steel corrosion and thick-walled cylinder models for the prediction of stresses induced by the accumulating corrosion products in the concrete cover. The proposed approach is illustrated on the life cycle design and management of highway bridge decks using normal and high performance concrete. It is shown that the high performance concrete deck alternative is a Pareto optimum, while the normal concrete deck is found to be a dominated solution in terms of life cycle costs and environmental impacts.     

A holistic approach to asset management in the Netherlands

The objective of the national agency for road and water infrastructure in the Netherlands is to deliver best service to the public at lowest life cycle cost, given public acceptable risk. This requires a sound and well-structured system of management and maintenance. Such a system consists of elements such as reliable asset data, long-term performance based maintenance programmes, clear steering objectives and transparent procurement strategies. Asset management will make it possible to show what network performance can be achieved, at which costs and with which associated risks. A model defining an asset owner role, an asset manager role and a service provider role was chosen as a governing principle for the design of the asset management process. The design of the process described in the paper sets the boundary conditions to carry out projects with the minimum amount of hindrance for the user while involving the market in the best possible way.     

Life-cycle performance,management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1

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.     

An augmented Lagrangian decomposition approach for infrastructure maintenance and rehabilitation decisions under budget uncertainty

This article addresses a general class of infrastructure management problems raised in maintenance and rehabilitation scheduling. Infrastructure agencies usually face budget uncertainties that eventually lead to suboptimal scheduling planning if maintenance decisions are made without taking the uncertainty into consideration. It is important for decision makers to adopt maintenance and rehabilitation scheduling policies that consider all budget scenarios. The authors propose a multistage, stochastic programming model to address this problem. The authors also develop a solution procedure using the augmented Lagrangian decomposition algorithm. A case study exploring the computational characteristics of the proposed algorithm is conducted and the benefit ofusing the stochastic programming approach is discussed.     

A greenhouse gas assessment of a stadium in Australia

A greenhouse gas (GHG) life cycle assessment (LCA) was performed on a stadium used for sporting events in a subtropical region in Australia. Inventories for the construction and operation of a stadium are presented and the GHG emissions from construction, operations and end-of-life waste management are assessed against the attendance of one person at one event. The inclusion of additional economic activities, patron travel, LCA methodology, attendance and stadium life-time assumptions are likely to affect the overall magnitude of the GHG emissions of one person's attendance. The assessment shows that the stadium operation accounted for 72.5% of GHG emissions, with the operation of baseload heating, ventilation and cooling, lighting and refrigeration systems dominating. The best opportunity to reduce GHG emissions is to reduce the need for the continual operation of these systems. Construction impacts account for 24.7% of impacts, while replacement materials, end-of-life management of materials are relatively insignificant, contributing to less than 3% of life cycle GHG emissions.     

Life cycle assessment of municipal solid waste management with regard to greenhouse gas emissions: Case study of Tianjin, China

The environmental impacts of municipal solid waste (MSW) management have been highlighted in China, due to the continually increasing amount of MSW being generated and the limited capacity of waste treatment facilities. Of particular interest is greenhouse gas (GHG) mitigation, aided by the Kyoto Mechanisms. China is an important case study for this global issue; however, an analysis of the entire life cycle of MSW management on GHG emissions is not available for China. This study evaluates the current and possible patterns of MSW management with regard to GHG emissions, using life cycle assessment (LCA), based on the Tianjin case. We assess the baseline scenario, reflecting the existing MSW management system, as well as a set of alternative scenarios, five exploring waste treatment technology innovations and one exploring integrated MSW management, to quantitatively predict potentials of GHG mitigation for Tianjin. Additionally, a sensitivity analysis is used to investigate the influence of landfill gas (LFG) collection efficiency, recycling rate and methodological choice, especially allocation, on the outcomes. The results show GHG emissions from Tianjin's MSW management system amount to 467.34 Mg CO₂ eq. per year, based on the treatment of MSW collected in the central districts in 2006, and the key issue is LFG released. The integrated MSW management scenario, combining different improvement options, shows the highest GHG mitigation potential. Given the limited financial support and the current waste management practice in Tianjin, LFG utilization scenario would be the preferred choice. The sensitivity analysis of recycling rate shows an approximately linear relation of inverse proportion between recycling rate and total GHG emissions. Kitchen waste composting makes a considerable contribution to total GHG emissions reduction. Allocation choices result in differences in total quantitative outcomes, but preference orders and contributions analysis are found to be robust, suggesting LCA can support decision making.     

A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy

Cost-effective maintenance of infrastructure systems within an acceptable level of safety and performance is the major concern of managing agencies. Recent maintenance approaches have offered two distinct maintenance policies: time-based maintenance (TBM) and condition-based maintenance (CBM). This paper compares the two policies under different cost environments for stochastically deteriorating infrastructures. The performance of TBM and CBM is evaluated from the viewpoint of condition transition and life cycle cost. We found the optimal maintenance solutions for TBM and CBM using dynamic programming and performed a simulation study. The simulation study showed that TBM causes some unexpected deterioration that leads to high cost, while CBM maintains a certain level of condition steadily under consistent inspection, which enables steady spending at the management level. The life cycle cost under CBM is relatively symmetric and has a more concentrated distribution than TBM, which has a large number of outliers from unexpected deteriorations. Finally, we evaluated the life cycle cost with a change in the inspection–repair cost ratio to find the most appropriate cost environment for each maintenance policy. While CBM needs periodic inspections, it still has more advantages than TBM when the inspection cost is relatively low.     

Life cycle emissions analysis of two nZEB concepts

The net-zero emissions building (nZEB) performance is investigated for building operation (EO) and embodied emissions in materials (EE) for Norway's cold climate. nZEB concepts for new residential and office buildings are conceived in order to understand the balance and implications between operational and embodied emissions over the building's life. The main drivers for the CO₂ equivalent (CO₂e) emissions are revealed for both building concepts through a detailed emissions calculation. The influence of the CO₂e factor for electricity is emphasized and it is shown to have significant impact on the temporal evolution of the overall CO₂e emissions balance. The results show that the criterion for zero emissions in operation is easily reached for both nZEB concepts (independent of the CO₂e factor considered). Embodied emissions are significant compared to operational emissions. It was found that an overall emissions balance including both operational and embodied energy is difficult to reach and would be unobtainable in a scenario of low carbon electricity from the grid. In this particular scenario, the net balance of emissions alone is nonetheless not a sufficient performance indicator for nZEB.