Ru An

Predicting the tamping cycles of railway track sections based on track geometry deterioration rules is necessary to reasonably allocate the limited tamping maintenance resources. Existing research on track geometry deterioration modeling for tamping cycle prediction lacks simultaneous consideration of the deterioration characteristics including heterogeneity, uncertainty, and historical dependence, thereby limiting the accuracy of the prediction results. Thus, this study considers a 200 m track segment as the basic object and uses the power-time-transformed Wiener process to develop a deterioration prediction model for the longitudinal level of a segment between two adjacent tamping operations. Moreover, it individually estimates the model parameters for each track segment to predict the tamping maintenance cycle for each segment combined with the tamping maintenance threshold of the longitudinal level index. Finally, through a case study of the Chinese Lanxin Railway line, the effectiveness of the proposed model and different parameter estimation methods is assessed.

Decision-making surrounding asset renewal is essential for the efficient use of renewal resources and safe operation of urban rail transit. In this study, major problems in the current management of urban rail industries in countries with the same problems as those in China were analysed, and in response, a renewal management framework based on service life estimation was proposed to provide adequate decision-making support for urban rail transit assets. In this framework, the cumulative failure frequency of an asset is used to indicate its health condition, and considering the uncertainties and heterogeneities in the deterioration process of assets, a Poisson–Weibull process model-based methodology was developed to estimate the service and residual lives of each asset, which are then employed in analysing its renewal demand and renewal period. Finally, the model is validated through an empirical study of rail renewal in the Beijing Metro. Our evaluation demonstrates that the proposed framework can estimate each asset’s service life accurately and can be used by asset management personnel to establish reasonable renewal plans and provide decision-making support for a scientifically informed resource allocation, thus mitigating major problems in current management practices.

The railway track geometry condition is a key factor influencing the safety and comfort of train operations, and controlling the tamping cycles of railway tracks. For the scientific disposition of limited maintenance resources, railway infrastructure managers need to predict the tamping cycles based on an accurate grasp of track geometry degradation rules. Taking each 200-m track segment as a research object, the authors analyze the uncertainty and heterogeneity of track geometry degradation based on the discrete evaluation of the track geometry condition. On this basis, an improved track geometry degradation model using Weibull distributions is proposed to accurately estimate the tamping cycle of each track segment. By considering several heterogeneous factors in the model, individualized modeling for different track segments is realized. The developed model was verified by a case study of 100 track segments of the Chinese Lanxin railway line within the jurisdiction of the Lanzhou Railway Bureau. The estimation results of model parameters reflect the influence of various heterogeneity factors on the track geometry deterioration processes and the tamping cycles of track segments. The accuracy of the tamping cycle estimation results indicates that the proposed approach has significance for guiding the management of tamping maintenance of railway track segments.

Because steel rail is one of the most fundamental components of railway operations, the accurate estimation of residual rail service life is of great significance in ensuring the safe operation of railways. In addition, maintenance expenses must be minimized in a manner that allows limited railroad resources to be optimally allotted. In this study, the typical types of continuous rail segments on a rail line are classified into non-sharply curved rail segments and sharply curved rail segments. Using these classifications, a model for estimating the residual service lives of rail segments using a discrete-state conditional probability method is proposed based on an analysis of rail deterioration characteristics. The model considers several heterogeneous factors to determine their influence on the deterioration process and is shown to be capable of estimating the residual service lives of rail segments. Finally, the model is validated through a case study of the Beijing Metro, using inspection records of rail defects in conjunction with heterogeneous factor data to predict the service life of the rail, which is then compared with its actual service life. The model is found to show good agreement with the rail inspection and maintenance records of the Beijing Metro, indicating its appropriateness for use by railroad management in allocating future rail maintenance resources.