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The purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.

Six statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.

The results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.

There is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.

The originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.

Finding the optimal solution to address problems in sewer management systems has always challenged asset managers. An understanding of deterioration mechanisms in sewers can help asset managers in developing prediction models for estimating whether or not sewer collapse is likely. The effective use of deterioration prediction models along with the development and use of life cycle cost analysis (LCCA) can contribute to the goals of reducing construction, operation and maintenance costs in sewer systems. When sewer system maintenance/rehabilitation options are viewed as investment alternatives, it is important, and in some cases, imperative, to make decisions based on life cycle costs instead of relying totally on initial construction costs. The objective of this paper is to discuss the application of deterioration modelling and life cycle cost principles in sewer system management, and to explore the role of the Markov chain model in decision making regarding sewer rehabilitation. A test case is used to demonstrate the application of the Markov chain decision model for sewer system management. The analysis includes evaluation of this concept using dynamic programming and the policy improvement algorithm.

This paper is about best practice in managing legacy drainage assets to support sustainable urban regeneration. The purpose of this paper is to describe best practice sewer asset management (AM) and to adjust the current reactive maintenance approach for sewers, to one that accommodates long-term operational and town planning needs. The development of an improved sewer deterioration model (DM) provided an important tool for this.

This research adopts a mixture of qualitative and quantitative approaches to analyse a total network length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved DM. These models were used as inputs into a proactive AM approach that improves upon recommendations in the Sewerage Rehabilitation Manual developed by Water Research Centre.

This is a paradigm shift and goes beyond the current culture of OFWAT (Water Services Regulation Authority) supervision, five-year asset management period and occasional environmental penalties. A new legislative model may be needed; especially because a report by UKWIR (Water Industry Research) in 2015 identified that nationally the rate of sewer network deterioration is outpacing available investment and significant health problems may arise in addition to those from developmental pressures.

The authors have researched and managed old sewer networks and present a review of the new issues raised by intensive development, particularly for the London region, but applicable elsewhere, and how these must lead to a modified risk, and novel incentive-based approach to AM, if the system is not to fail.

Large, legacy databases of several decades of sewer network performance records have been combined and analysed as stratified, heterogeneous sets with Gaussian distributions; thereby improving on previous assumptions of homogeneous data. The resulting rigorous DMs are the foundation of new approaches to sustainable risk management of large urban networks.

This paper proposes a decision‐making framework to assist asset managers in decision making regarding sewer maintenance/rehabilitation (M&R) plans under constraints of limited access to sewer condition data. It discusses the application of probabilistic dynamic programming in conjunction with a Markov chain model to analyze the life cycle cost of combined sewer systems. M&R issues have traditionally been addressed with a crisis‐based approach, but this study contributes to sewer infrastructure management efforts in developing a management system based on life cycle cost analysis. The framework includes the optimal M&R techniques for sewer projects and the optimal times of application. The role of simulation is also explored to obtain the variability of the total cost. By knowing the expected costs and their variabilities, a deeper understanding of life cycle costs of sewer infrastructure can be obtained. The model’s capability is enhanced further by testing its sensivitity to varying discount and inflation rates.

We investigate discrepancies between willingness to pay (WTP) and willingness to accept (WTA) in the context of a stated choice experiment. Using data on customer preferences for water services where respondents were able to both ‘sell’ and ‘buy’ the choice experiment attributes, we find evidence of non-linearity in the underlying utility function even though the range of attribute levels is relatively small. Our results reveal the presence of significant loss aversion in all the attributes, including price. We find the WTP–WTA schedule to be asymmetric around the current provision level and that the WTP–WTA ratio varies according to the particular provision change under consideration. Such reference point findings are of direct importance for practitioners and decision-makers using choice experiments for economic appraisal such as cost–benefit analysis, where failure to account for non-linearity in welfare estimates may significantly over- or under-state individual's preferences for gains and avoiding losses respectively.

The combined effects of several complex phenomena cause the deterioration of elements in steel hydraulic structures (SHSs) within the US lock system: corrosion, cracking and fatigue, impact and overloads. Predicting the future condition state of these structures by the use of current condition state inspection data can be achieved through the probabilistic chain deterioration model. The purpose of this study is to derive the transition probability matrix using final elements modeling of a miter gate.

If predicted accurately, this information would yield benefits in determining the need for rehabilitation or replacement of SHS. However, because of the complexity and difficulties on obtaining sufficient inspection data, there is a lack of available condition states needed to formulate proper transition probability matrices for each deterioration case.

This study focuses on using a three-dimensional explicit finite element analysis (FEM) of a miter gate that has been fully validated with experimental data to derive the transition probability matrix when the loss of flexural capacity in a corroded member is simulated.

New methodology using computational mechanics to derive the transition probability matrices of navigation steel structures has been presented.

The difficulty of deriving the transition probability matrix to perform a Markovian analysis increases when limited amount of inspection data is available. The used state of practice FEM to derive the transition probability matrix is not just necessary but also essential when the need for proper maintenance is required but limited amount of the condition of the structural system is unknown.

Several rehabilitation planning methods are reported in the literature for public infrastructures, such as bridges, pavements, sewers, etc. These methods, however, are limited to specific types of infrastructures. The purpose of the present research is to develop a novel and generic method for Maintenance and Rehabilitation Planning for Public Infrastructure (M&RPPI), which aims at determining the optimal rehabilitation profile over a desired analysis period.

The M&RPPI method is based on life‐cycle costing (LCC) with probabilistic and continuous rating approach for condition states. The M&RPPI uses a new approach of “dynamic” Markov chain to represent the deterioration mechanism of an infrastructure and the impact of rehabilitation interventions on such infrastructure. It also uses genetic algorithm (GA) in conjunction with Markov chains in order 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 new method, which generates lower LCC, is found practical in providing a complete M&R plan over a required study period, compared to a stationary decision policy with the traditional MDP. In addition, GA is found useful in the optimization process and overcomes the computational difficulties for large combinatorial problems.

The implementation of the developed models is limited to only four alternatives/actions. However, the developed models and framework are superior for MDP.

The developed methodology and model play essential roles in the decision‐making process.

The new method is beneficial to researchers and practitioners. It is developed for a single facility; however, it provides a major step towards a broader infrastructure management system and capital budgeting problems.

The purpose of this paper is to present a methodology to evaluate the capital investments in infrastructure projects managed by private operators considering uncertainties in the operation and maintenance of the infrastructure components.

The methodology described in this paper is based on two major sources of information: deterioration curves of the infrastructure systems obtained from Markov chain models and the value of flexibility obtained from a real options analysis.

Using this methodology, it is possible to determine whether there is value if project managers adopt flexible strategies in determining capital investments. These strategies refer to the opportunities of postponing, deferring or canceling capital investments required to maintain the operation of the infrastructure systems.

The model utilizes Monte Carlo simulation and real options analysis to overcome the complexities associated with the solution of the differential equations that represent the variability of the main factors in the project cash flow.

The methodology presented in this paper can be used by public officials, private investors, and asset managers to determine the value of flexibility associated with the strategies required to maintain the operation of infrastructure assets.

The purpose of this paper is to present a socio-technical approach to modeling the behavior of roadway users, asset managers, and politicians toward roadway performance and asset management. This approach models the complex interactions that occur between these agents in a complex system. Most modeling approaches in the domain of infrastructure asset management take a purely asset-centric approach and fail to address these socio-technical interactions.

Interactions among political decision makers, asset management strategy developers, and road users are modeled using a game-theoretic approach. The interactions are modeled as a non-cooperative game in which politicians, asset managers, and road users are the main players. Each player is autonomous and aims to come up with the set of moves to maximize their respective level of satisfaction in response to other players’ moves. Multi-attribute utility theory is used to deal with multitude of players’ goals, and the Nash equilibria of the game are south out to develop appropriate strategies for different players.

An illustrative example for a road network of a Canadian city is used to demonstrate the developed methodology. The developed methodology demonstrates how behaviors of various agents involved in the sphere of asset management impacts their collective decision-making behavior.

The developed framework provides asset managers and political decision makers with a valuable tool to evaluate the impact of public policy decisions related to asset managers on road performance and the overall satisfaction of road users.