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. 2025 Mar 22;25(7):1998.
doi: 10.3390/s25071998.

Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection

Jessada Sresakoolchai  1 , Chayutpong Manakul  1 , Ni-Asri Cheputeh  2
Affiliations

Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection

Jessada Sresakoolchai et al. Sensors (Basel). .

Abstract

Railway tight and wide gauges are critical factors affecting the safety and reliability of railway systems. Undetected tight and wide gauges can lead to derailments, posing significant risks to operations and passenger safety. This study explores a novel approach to detecting railway tight and wide gauges by integrating accelerometer data, machine-learning techniques, and building information modeling (BIM). Accelerometers installed on axle boxes provide real-time dynamic data, capturing anomalies indicative of tight and wide gauges. These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. The integration with BIM offers a spatial and temporal framework, enhancing the visualization and contextualization of detected issues. BIM's capabilities allow for the precise mapping of tight- and wide-gauge locations, streamlining maintenance workflows and resource allocation. Results demonstrate high accuracy in detecting and predicting tight and wide gauges, emphasizing the reliability of machine-learning models when coupled with accelerometer data. This research contributes to railway maintenance practices by providing an automated, data-driven methodology that enhances the proactive identification of tight and wide gauges, reducing the risk of derailments and maintenance costs. Additionally, the integration of machine learning and BIM highlights the potential for comprehensive digital solutions in railway asset management.

Keywords: accelerometer data; building information modeling; digital asset management; machine learning; railway maintenance; tight and wide gauge.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Track gauge measurement.
Figure 2
Figure 2
Examples of MBS model based on Manchester benchmark.
Figure 3
Figure 3
Shape of outputs from the MBS models or the features for the machine-learning model.
Figure 4
Figure 4
Shapes of track gauge anomalies.
Figure 5
Figure 5
Examples of exported ABAs: (a) X-longitudinal direction, (b) Y-lateral direction, and (c) Z-vertical direction.
Figure 5
Figure 5
Examples of exported ABAs: (a) X-longitudinal direction, (b) Y-lateral direction, and (c) Z-vertical direction.
Figure 6
Figure 6
Example of the CNN architecture.
Figure 7
Figure 7
Workflow of the developed approach (a) for integrating machine learning and BIM, and (b) BPMN (Business Process Model and Notation).
Figure 7
Figure 7
Workflow of the developed approach (a) for integrating machine learning and BIM, and (b) BPMN (Business Process Model and Notation).
Figure 8
Figure 8
Developed a BIM model for the railway project.
Figure 9
Figure 9
Exported BIM model as an IFC file.
Figure 10
Figure 10
Examples of pseudo-code for (a) exporting information from the BIM model and (b) updating the information in the BIM model.
Figure 10
Figure 10
Examples of pseudo-code for (a) exporting information from the BIM model and (b) updating the information in the BIM model.
Figure 11
Figure 11
Integrated information in the BIM model for the maintenance aspects.
Figure 12
Figure 12
Training and testing losses.
Figure 13
Figure 13
Training and testing accuracies.
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