Výsledky vyhledávání - "Youwai, Sompote"

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Explainable Artificial Intelligent (XAI) for Predicting Asphalt Concrete Stiffness and Rutting Resistance: Integrating Bailey's Aggregate Gradation Method

Autor: Kongkitkul, Warat, Youwai, Sompote, Khamsoy, Siwipa, Feungfung, Manaswee

This study employs explainable artificial intelligence (XAI) techniques to analyze the behavior of asphalt concrete with varying aggregate gradations, focusing on resilience modulus (MR) and dynamic stability (DS) as measured by wheel track tests. Th

Externí odkaz: http://arxiv.org/abs/2410.21298

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Developing an Explainable Artificial Intelligent (XAI) Model for Predicting Pile Driving Vibrations in Bangkok's Subsoil

Autor: Youwai, Sompote, Pamungmoon, Anuwat

This study presents an explainable artificial intelligent (XAI) model for predicting pile driving vibrations in Bangkok's soft clay subsoil. A deep neural network was developed using a dataset of 1,018 real-world pile driving measurements, encompassi

Externí odkaz: http://arxiv.org/abs/2409.05918

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YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism

Autor: Youwai, Sompote, Chaiyaphat, Achitaphon, Chaipetch, Pawarotorn

Maintaining road pavement integrity is crucial for ensuring safe and efficient transportation. Conventional methods for assessing pavement condition are often laborious and susceptible to human error. This paper proposes YOLO9tr, a novel lightweight

Externí odkaz: http://arxiv.org/abs/2406.11254

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Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil

Autor: Youwai, Sompote, Thongnoo, Chissanupong

This paper presents a novel deep learning model based on the transformer architecture to predict the load-deformation behavior of large bored piles in Bangkok subsoil. The model encodes the soil profile and pile features as tokenization input, and ge

Externí odkaz: http://arxiv.org/abs/2312.03041

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Computer Vision for Particle Size Analysis of Coarse-Grained Soils

Autor: Youwai, Sompote, Makam, Parchya

Particle size analysis (PSA) is a fundamental technique for evaluating the physical characteristics of soils. However, traditional methods like sieving can be time-consuming and labor-intensive. In this study, we present a novel approach that utilize

Externí odkaz: http://arxiv.org/abs/2311.06613

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