Work

LSTM and TCN application for airport surface distress detection

Year: 2025

Type: article

Abstract: This paper evaluates the feasibility of using smartphone accelerometers to identify and categorize airport pavement distresses. Using experimental measurements taken on a test road section, we tested the smartphone accelerometers to recognize and categorize selected distress patterns. In our work, we investigated the capacity of neural networks with a Long Short-Term Memory (LSTM) layer with normalized data weighted and not weighted, bidirectional LSTM, and Temporal Convolutional Networks (TCNs). The networks tested for sequence data classification displayed considerably high accuracy. However, many associations of sequence versus distress were needed to adequately complete the training process. In contrast, the TCNs we used for sequence-to-sequence classification showed lower accuracy. However, a much smaller data set was sufficient to complete the training. As a consequence, we chose the TCN to implement the practical application. Both algorithms demonstrated high accuracy on training and validation data and performed well on other independent test samples.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.105708

Authors: Ludek Cicmanec, Adam Kubica, Jiri Maslan

Institution: University of Defence

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,940