Road operation phase sustainability indicator as a response to the climate change phenomena

Road infrastructure is a major contributor of greenhouse gas (GHG) around the world. Once constructed, a road becomes a part of a road network and is subjected to recurrent maintenance/rehabilitation activities. Studies to date are mostly aimed at the development of sustainability indicators that deal with the material and construction phases of a road when it is constructed. The operation phase is infrequently studied and there is a need for sustainability indicators to be developed relating to this phase to better understand the GHG emissions as a proper response to the climate change phenomena. During the operation phase, maintenance/rehabilitation activities are undertaken based on certain agreed intervention criteria that do not include environmental implications relating to the climate change aspect properly. Availability of appropriate indicators may, therefore, assist in sustainable road asset maintenance management. This paper presents the findings of a literature based study and has proposed a way forward to develop a key “road operation phase” environmental indicator, which can contribute to road operation phase carbon footprint management based on a comprehensive road life cycle system boundary model. The proposed indicator can address multiple aspects of high impact road operation life environmental components such as: pavement rolling resistance, albedo, material, traffic congestion and lighting, based on availability of relevant scientific knowledge. Development of the indicator to appropriate level would offset the impacts of these components significantly and contribute to sustainable road operation management.

Report : stochastic analysis of road asset data for maintenance cost prediction

Reliable budget/cost estimates for road maintenance and rehabilitation are subjected to uncertainties and variability in road asset condition and characteristics of road users. The CRC CI research project 2003-029-C ‘Maintenance Cost Prediction for Road’ developed a method for assessing variation and reliability in budget/cost estimates for road maintenance and rehabilitation. The method is based on probability-based reliable theory and statistical method. The next stage of the current project is to apply the developed method to predict maintenance/rehabilitation budgets/costs of large networks for strategic investment. The first task is to assess the variability of road data. This report presents initial results of the analysis in assessing the variability of road data. A case study of the analysis for dry non reactive soil is presented to demonstrate the concept in analysing the variability of road data for large road networks. In assessing the variability of road data, large road networks were categorised into categories with common characteristics according to soil and climatic conditions, pavement conditions, pavement types, surface types and annual average daily traffic. The probability distributions, statistical means, and standard deviation values of asset conditions and annual average daily traffic for each type were quantified. The probability distributions and the statistical information obtained in this analysis will be used to asset the variation and reliability in budget/cost estimates in later stage. Generally, we usually used mean values of asset data of each category as input values for investment analysis. The variability of asset data in each category is not taken into account. This analysis method demonstrated that it can be used for practical application taking into account the variability of road data in analysing large road networks for maintenance/rehabilitation investment analysis.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Infrastructure asset : developing maintenance management capability

Purpose Maintenance management is a core process in infrastructure asset management. Infrastructure organisations must constantly strive to ensure the effectiveness of this process in order to obtain the greatest lifetime value from their infrastructure assets. This paper aims to investigate how infrastructure organisations can enhance the effectiveness of their maintenance management process. Approach This study utilised multiple case studies as the research approach. The case organisations were asked to identify the challenges faced in the maintenance process and the approaches they have adopted to overcome these challenges. Analysis of these findings, together with deductive reasoning, leads to the development of the proposed capability needed for effective maintenance management process. Findings The case studies reveal that maintenance management process is a core process in ensuring that infrastructure assets are optimally and functionally available to support business operations. However, the main challenge is the lack of skilled and experienced personnel to understand and anticipate maintenance requirement. A second challenge is the reduced window of time available to carry out inspection and maintenance works. To overcome these challenges, the case organisations have invested in technologies. However, technologies available to facilitate this process are complex and constantly changing. Consequently, there is a need for infrastructure organizations to develop their technological absorptive capability, i.e. the ability to embrace and capitalize on new technologies to enhance their maintenance management process. Originality/Value This paper is original in that it provides empirical evidence to identify technological absorptive capability as core to improving the maintenance management process. The findings are valuable because it sheds light on where infrastructure organisation, regardless of whether they are privately or publicly owned, should channel their scarce resources. The development of the core capability will ensure that the maintenance process can contribute value to their organisation.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.