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The purpose of this paper is to develop a condition-based maintenance (CBM) model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process.

Regarding environmental changes as random shocks, the effect of environmental changes on the deterioration process is considered. Then, non-homogeneous Poison process and non-stationary gamma process are introduced to model the deterioration pitting initiation process and the deterioration pitting growth process, respectively. Finally, based on the deterioration model, a CBM policy is put forward to obtain the optimal inspection interval by minimizing the expected maintenance cost rate. Numerical simulations are given to optimize the performance of the deteriorating system. Meanwhile, comparisons between a single-stage deterioration model and a two-stage deterioration model are conducted to demonstrate the application of the proposed approach.

The result of simulation verifies that the deterioration rate is not constant in the life cycle and is affected by the environment. Furthermore, the result shows that the two-stage deterioration model proposed makes up for the shortage of single-stage deterioration models and can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models.

In practical situations, except for normal deterioration caused by internal factors, many systems are also greatly influenced by the random shocks during operation, which are probably caused by the environmental changes. What is more, most systems have self-protection ability in some extent that protects them to keep running as new ones for some time. Under such circumstances, the two-stage deterioration model proposed can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models. In the combination with the bootstrap estimation, the paper obtains the life distributions with approximate 95 percent confidence intervals which can provide valuable information for practical system maintenance scheduling.

This paper presents a new CBM model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process. Considering the effect of the environmental change on the system deterioration process, a two-stage deterioration model with environmental change factors is proposed to describe the system deterioration.

Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon.

The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems.

The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs.

On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

Pavement deterioration prediction models play a crucial role in determining maintenance strategies and future funding needs. While deterioration prediction models have been studied extensively in the past, applications of these models to local street networks have been limited. This study aims to address this gap by sharing the results of network level deterioration prediction models developed at a local level.

Network level pavement deterioration prediction models are developed using Markov chains for the local street network in Syracuse, New York, based on pavement condition rating data collected over a 15-year time period. Transition probability matrices are generated by calculating the percentage of street sections that transition from one state to another within one duty cycle. Bootstrap sampling with replacement is used to numerically generate 95% confidence intervals around the transition probability values.

The overall local street network is divided into three cohorts based on street type (i.e. avenues, streets and roads) and two cohorts based on pavement type. All cohorts demonstrated very similar deterioration trends, indicating the existence of a fast-paced deterioration mechanism for the local street network of Syracuse.

This study contributes to the body of knowledge in deterioration modeling of local street networks, especially in the absence of key predictor variables. Furthermore, this study introduces the use of bootstrap sampling with replacement method in generating confidence intervals for transition probability values.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

This article aims to analyze strategically optimal maintenance actions for a multi‐component system whose deterioration is observed through a monitoring system set in place to support condition‐based maintenance.

Deterioration of a multi‐component system is modeled by a continuous‐time jump diffusion model which incorporates interaction between the components of the system. A simulation‐based optimization heuristic is developed to obtain strategically optimum maintenance actions. The methodology is applied to an illustrative example.

The article finds that the framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. The optimal solution for the illustrative example recommends a provider to perform a variety of opportunistic maintenance.

In this article, a framework is developed to determine strategically optimal maintenance actions for a multi‐component system whose deterioration is observed in real‐time through embedded monitoring units set in place to support condition‐based maintenance (CBM). The framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. A strategically optimal maintenance policy can then be enhanced to develop a detailed tactical maintenance strategy. This approach is expected to benefit the management of long‐term service agreements, where a service contract is sold bundled with a product, which makes a provider responsible for maintaining the product over a specified contract period.

Besides a tactical approach for performing maintenance, in order to stay profitable in the long‐run, a decision maker needs to assess the strategic performance of maintenance strategies adopted. This framework is a first attempt to facilitate this analysis at a strategic level for a monitoring‐enabled multi‐component system.

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.

This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.

A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.

Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.

The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.

The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.

Mid-rise steel moment-resisting frames (MRFs) with intermediate ductility are a major part of conventional residential buildings in Iran. According to Iranian seismic design codes, in this resisting system, considering the strong-column/weak-beam (SCWB) criterion is not mandatory. Where a metal deck ceiling system is used, the composite action of a concrete slab and steel beams could change the collapse mechanism of the structure, especially in the MRFs with intermediate ductility. The purpose of this paper is to investigate the influence of the composite action in the seismic collapse risk of this type of structures. Seismic collapse risk assessment can be carried out by using simplified pushover-based methods. In these methods, the cyclic deterioration of an equivalent single degree of freedom (ESDoF) system must be considered when the modified Ibarra–Medina–Krawinkler is used for nonlinear modeling of MRFs. Accordingly, a modified method is developed to use in simplified collapse risk assessment process. For these purposes, two mid-rise MRFs with intermediate ductility located in Tehran have been selected as case studies. The results confirm that the composite action is very effective in collapse risk value in the steel MRFs in which their SCWB ratio is less than 1. Moreover, the proposed approach of considering the cyclic deterioration of ESDoF systems increases the accuracy of the simplified collapse assessment approaches.

Identifying seismically vulnerable buildings to collapse requires using robust methods. These methods can be simplified based on pushover analysis methods. An attempt was made to apply one of these approaches for steel MRFs with intermediate ductility. In these frames, the composite action of a concrete slab and steel beams could change the collapse mechanism. Here, two MRFs were investigated in order to assess this effect on collapse risk value. This process was done by modifying the SPO2IDA method as a simplified collapse capacity evaluation approach by developing a relationship to consider the cyclic deterioration effects for the ESDoF systems.

The results showed that it is necessary to consider the slab effects in the analytical model in the collapse assessment process of MRFs with intermediate ductility, especially in the condition in which the SCWB ratios of the frame are less than 1. Furthermore, by utilizing the proposed method of considering the ESDoF cyclic deterioration, the error values of the SPO2IDA program were reduced significantly. Moreover, estimating the collapse risk parameters shows that the utilized simplified method presents suitable accuracy and could be an acceptable approach to collapse risk assessment of mid-rise steel MRFs.

The influence of the composite action in seismic collapse risk of MRFs with intermediate ductility is investigated. Also, a modified relationship is developed to consider the deterioration effects on the ESDoF parameters used in simplified collapse risk assessment process. Also, a framework is presented for utilized methodology.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

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.