LSTM and TCN application for airport surface distress detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564687" target="_blank" >RIV/60162694:G43__/26:00564687 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.rineng.2025.105708" target="_blank" >https://doi.org/10.1016/j.rineng.2025.105708</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.105708" target="_blank" >10.1016/j.rineng.2025.105708</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LSTM and TCN application for airport surface distress detection

Popis výsledku v původním jazyce

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 dataset 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.

Název v anglickém jazyce

LSTM and TCN application for airport surface distress detection

Popis výsledku anglicky

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 dataset 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

21100 - Other engineering and technologies

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

RESULTS IN ENGINEERING

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

27

Číslo periodika v rámci svazku

27

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

105708

Kód UT WoS článku

001513593900010

EID výsledku v databázi Scopus

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