Big-data driven assessment of railway track and maintenance efficiency using Artificial Neural Networks

Popov, K.; Bold, R. De; Chai, H.-K.; Forde, M. C.; Ho, Carlton L.; Hyslip, James P.; Kashani, Hamed Faghihi; Long, Paul; Hsu, Sin Sin

doi:10.1016/j.conbuildmat.2022.128786

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Within the artificial neural network model, five variables (equipment type, preventive maintenance frequency, material quality, life cycle, line status) were included as input factors which influence equipment faults, while the outputs were represented by the number of failures. Lastly, Popov et al [21] used an Artificial Intelligence (AI) technique to analyze the track quality big data of a high-speed line in the UK, using a dataset with more than 15 years of track geometry. More specifically, an ANN model was developed to identify segments of the railway track where the condition was either improved or deteriorated during the time elapsed between two inspection runs.…”

Section: Related Studiesmentioning

confidence: 99%

Combined Optimization of Maintenance Works and Crews in Railway Networks

Gkonou,

Nisyrios,

Gkiotsalitis

2023

Applied Sciences

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This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.

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Section: Related Studiesmentioning

confidence: 99%

Combined Optimization of Maintenance Works and Crews in Railway Networks

Gkonou,

Nisyrios,

Gkiotsalitis

2023

Applied Sciences

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“…However, challenges such as overfitting, vanishing gradients, and computational complexity persist, necessitating ongoing research and refinement. Nonetheless, with their ability to learn from data and generalize patterns, BP neural networks continue to drive advancements in artificial intelligence, empowering solutions to increasingly intricate real-world problems [14].…”

Section: Introductionmentioning

confidence: 99%

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Xiaoguang Hu

2024

jes

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Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.

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“…Infrastructure management companies need to systematically carry out different maintenance works to ensure that railroad networks are in good condition for operations [4][5][6]. These works range from the inspection of different track elements such as the rails, ballast, tightening bolts or sleepers to the management of the different repair works necessary to keep the infrastructure in operation under safety conditions [7][8][9]. Although many inspections are still performed visually by human operators, the current trend is to automate such work, through on-board sensors on railway locomotives, draisines or track trolleys, and artificial intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Validation of Solid-State LiDAR Measurement System for Ballast Geometry Monitoring in Rail Tracks

et al. 2023

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The inspection and maintenance of track ballast are fundamental tasks for the preservation of the condition of railway networks. This work presents an application based on a low-cost solid-state LiDAR system, which allows the user to accurately measure the ballast geometry from a mobile inspection trolley or draisine. The solid-state LiDAR system, the LiVOX Avia, was validated on a test track through comparison with a traditional static LiDAR system, the Faro Focus 3D. The results show a standard deviation of around 6 mm for the solid-state LiDAR system. The LiVOX system also provides the capability to measure the ballast digital elevation model and profiles. The LiVOX results are in agreement with those obtained from the Faro Focus. The results demonstrate that the LiVOX system can sufficiently measure even the displacement of a single layer of ballast stones typically between 2.5 cm and 5 cm. The data provided can be easily digitalized using image processing tools and integrated into geographic information systems for infrastructure management.

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Big-data driven assessment of railway track and maintenance efficiency using Artificial Neural Networks

Cited by 10 publications

References 24 publications

Combined Optimization of Maintenance Works and Crews in Railway Networks

Combined Optimization of Maintenance Works and Crews in Railway Networks

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Validation of Solid-State LiDAR Measurement System for Ballast Geometry Monitoring in Rail Tracks

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