Fatigue analysis of sign-support structures during transportation under road-induced excitations

Arabi, Saeed; Shafei, Behrouz; Phares, Brent

doi:10.1016/j.engstruct.2018.02.031

Cited by 23 publications

(4 citation statements)

References 17 publications

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“…Las grietas de la fatiga causada se desarrollan en la conexión del Brazo con el Poste y en la base del Poste, esto ocurre en un período de varios años. [11] Galopeo: Las acciones del galopeo provocan la aparición de oscilaciones en dirección perpendicular al flujo del viento. A diferencia de otros efectos este ocurre en miembros asimétricos (señales, letreros y otros anexos).…”

Section: Tipos Y Efectos Principales De Las Cargasunclassified

Análise de estruturas de suporte para sinalizações viárias

Méndez¹,

Perez²,

Méndez³

et al. 2021

Lat. Americ. J. of Develop.

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El cálculo de las estructuras de soporte para señalizaciones viales se realiza mediante la norma AASHTO con la cual se realiza fundamentalmente el chequeo a Fatiga, pero con ella no se llegan a conocer las tensiones von Mises, las tensiones tangenciales ni los desplazamientos en cualquier punto de la estructura. En el presente trabajo se determinan las cargas actuantes, así como sus combinaciones más críticas para el análisis. Se realiza el cálculo analítico a través de los métodos y las normas establecidas para ello y se confecciona un modelo de elementos finitos cuya simulación numérica complementa los resultados obtenidos por las normas. The calculation of the support structures for highway signs is carried out by means of the AASHTO standard, with which the Fatigue check is fundamentally carried out, but with it the von Mises stress, the tangential stress and the displacements at any point of the structure are not known. In the present work, the acting loads are determined, as well as their most critical combinations for the analysis. The analytical calculation is performed through the methods and standards established for it and a finite element model is created whose numerical simulation complements the results obtained by the standards.

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Section: Tipos Y Efectos Principales De Las Cargasunclassified

Análise de estruturas de suporte para sinalizações viárias

Méndez¹,

Perez²,

Méndez³

et al. 2021

Lat. Americ. J. of Develop.

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“…Seven classi ied loads are shown in Table II. In this time, X-axis is fore direction, Y-axis is lateral direction, and Z-axis is vertical direction [25,26,27,28].…”

Section: Frequency Domain Fatigue Analysismentioning

confidence: 99%

Comparison of Fatigue Analysis Results in the Frequency Domain using PSD data and specific load

Jang¹,

Cho²,

Lee³

et al. 2019

J. ICT des. eng. technol. sci.

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This study investigated the axial sensitivity of the aircraft structure to fatigue. Fatigue analysis for an aircraft needs to be done during a design stage to prevent fatigue failure during operation. Fatigue analysis can be done in a time domain or a frequency domain, and frequency domain analysis is more frequently used. Frequency domain analysis for an aircraft normally uses PSD data translated from the vibration experienced for its whole lifetime. If vibration information is insuf icient in the PSD, it is supplemented by adding speci ic loads. In this study, the analysis was done using the PSD data with 7 different additional loads. The life was sensitive to the magnitude and direction of the load, and some loads increase the life. It was found that the aircraft structure is safe from fatigue failure. Since the fatigue test has not yet been carried out, further testing should be done. Also, further study will investigate how to quantify the sensitivity ratio of direction and magnitude of loads under various conditions.

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“…Over the years, several methods have been developed by researchers and engineers to monitor civil infrastructure and evaluate its performance. Although conventional sensor‐based methods are still popular and effective (Amezquita‐Sanchez & Adeli, ; Amezquita‐Sanchez, Valtierra‐Rodriguez, & Adeli, ; Arabi & Shafei, 2019; Arabi, Shafei, & Phares, , ), recently new computer‐vision‐based methods, such as deep‐learning‐based computer vision solutions, have caught the attention of researchers in different areas of civil and infrastructure engineering (LeCun, Bengio, & Hinton, ). Although the main building block of deep learning, that is, neural networks, has been utilized by researchers for decades (Adeli, ), only recently has deep learning shown major breakthroughs due to increasingly affordable computing hardware, that is, graphics processing units (GPUs), and the increased availability of large‐scale datasets for training deep learning models (Russakovsky et al., ).…”

Section: Introductionmentioning

confidence: 99%

A deep‐learning‐based computer vision solution for construction vehicle detection

Arabi

Haghighat

Sharma

2020

Computer aided Civil Eng

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113

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This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep-learning-based solution for detecting construction vehicles. This paper places particular focus on the often-ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small-scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study's second phase comprised model optimization, application-specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real-time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated.This study validates the practicality of deep-learning-based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making.

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Fatigue analysis of sign-support structures during transportation under road-induced excitations

Cited by 23 publications

References 17 publications

Análise de estruturas de suporte para sinalizações viárias

Análise de estruturas de suporte para sinalizações viárias

Comparison of Fatigue Analysis Results in the Frequency Domain using PSD data and specific load

A deep‐learning‐based computer vision solution for construction vehicle detection

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