ASENN: attention-based selective embedding neural networks for road distress prediction

Babitha Philip, Zhenyu Xu, Hamad AlJassmi, Qieshi Zhang, Luqman Ali

Research output: Contribution to journalArticlepeer-review

Abstract

This study proposes an innovative neural network framework, ASENN (Attention-based Selective Embedding Neural Network), for the prediction of pavement deterioration. Considering the complexity and uncertainty associated with the pavement deterioration process, two fundamental frameworks, SEL (Selective Embedding Layer) and MDAL (Multi-Dropout Attention Layer), are combined to enhance feature abstraction and prediction accuracy. This approach is significant while analyzing the pavement deterioration process due to the high variability of the contributing deterioration factors. These factors, represented as tabular data, undergo filtering, embedding, and fusion stages in the SEL, to extract crucial features for an effective representation of pavement deterioration. Further, multiple attention-weighted combinations of raw data are obtained through the MDAL. Several SELs and MDALs were combined as basic cells and layered to form an ASENN. The experimental results demonstrate that the proposed model outperforms existing tabular models on four road distress parameter datasets corresponding to cracking, deflection, international roughness index, and rutting. The optimal number of cells was determined using different ablation settings. The results also show that the feature learning capabilities of the ASENN model improved as the number of cells increased; however, owing to the limited combination space of feature fields, extreme depths were not preferred. Furthermore, the ablation investigation demonstrated that MDAL can improve performance, particularly on the cracking dataset. Notably, compared with mainstream transformer models, ASENN requires significantly less storage and achieves faster execution speed.

Original languageEnglish
Article number164
JournalJournal of Big Data
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Deep learning
  • Pavement deterioration
  • Prediction models
  • Road distress parameters
  • Tabular data

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems and Management

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