Author:
Philip Babitha,Xu Zhenyu,AlJassmi Hamad,Zhang Qieshi,Ali Luqman
Abstract
AbstractThis 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.
Funder
United Arab Emirates University
Publisher
Springer Science and Business Media LLC
Subject
Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems
Reference27 articles.
1. Rogers M, Enright B. Highway engineering. 3rd ed. Hoboken: John Wiley & Sons, Incorporated; 2016.
2. American Society of Civil Engineers, 2021 Report Card for America’s Infrastructure: Solid Waste, 2022.
3. Sholevar N, Golroo A, Esfahani SR. Machine learning techniques for pavement condition evaluation. Autom Constr. 2022;136(Feb): 104190. https://doi.org/10.1016/j.autcon.2022.104190.
4. Yang X, Guan J, Ding L, You Z, Lee VCS, Mohd Hasan MR, Cheng X. Research and applications of artificial neural network in pavement engineering: a state-of-the-art review. J Traffic Transp Eng. 2021. https://doi.org/10.1016/j.jtte.2021.03.005.
5. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. https://doi.org/10.1023/A:1010933404324.
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