Review of Underground Storage Tank Condition Monitoring Techniques

Sheng, Ooi Ching; Ngui, Wai Keng; Hoou, Hui Kar; Hee, Lim Meng; Leong, M. Salman

doi:10.1051/matecconf/201925502009

Cited by 9 publications

(5 citation statements)

References 98 publications

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“…An average error of 6 test points 5, 10, 15, 20, 25, and 30 cm was 0.52% with a standard deviation 0.01 cm. It was showed that error value using the SRF05 sensor [1,5] smaller than the HCSR04P sensor in Table 1 as 0.62%. However, both sensors show the same result of the standard deviation.…”

Section: Resultsmentioning

confidence: 95%

“…The following data from ultrasonic sensor calibration using a standard steel ruler in Table 1. In [1,5], calibration data were obtained using a steel ruler for SRF05 ultrasonic sensor. An average error of 6 test points 5, 10, 15, 20, 25, and 30 cm was 0.52% with a standard deviation 0.01 cm.…”

Section: Resultsmentioning

confidence: 99%

“…The number of vehicles in the street increases rapidly. The petrol station facilitates the public to meet fuel needs [1]. Petrol station receives fuel supply from the nearest depot delivered using a measuring tank car, in Indonesia is called TUM.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design an Internet of Things Simulator of Fluid Volume Monitoring System for Petrol Station Underground Tank

Hidayanti¹

2020

IJST

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Objectives: This study aims to design an internet of things simulator of fluid volume monitoring system. Methods: The method used in this research includes simulator design and simulator testing. Monitoring system using an ultrasonic sensor, nodeMCU, ThingSpeak, and Virtuino. The value of ultrasonic sensors will be processed in the NodeMCU program resulting liquid level in the buried tank then sent through the internet to ThingSpeak. Findings: The result of monitoring the liquid level in a buried petrol station simulator uses ultrasonic sensor HCSR04P and NodeMCU v.1.0 from 08.00 pm to 02.00 am displayed on the site ThingSpeak with value 17.42 L.Setting alarm on Virtuino at 50% of maximum volume of 8.8 L. The experimental results the volume of fluid reduction 8.09 L has managed to activate alarm on the application Virtuino. Applications: This internet of things simulator of fluid volume monitoring system applicated for the petrol station underground tank.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Design an Internet of Things Simulator of Fluid Volume Monitoring System for Petrol Station Underground Tank

Hidayanti¹

2020

IJST

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“…Sheng et al explored health condition assessment methods for underground storage tanks, providing insights into the state of the art in this field [44]. Koch et al contributed a systematic review of damage identification and assessment techniques specifically tailored for concrete and asphalt pavements, deepening our understanding of pavement health monitoring [45].…”

Section: Motivationmentioning

confidence: 99%

A comprehensive review on health monitoring of joints in steel structures

Naresh,

Kumar,

Pal

et al. 2024

Smart Mater. Struct.

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Steel-framed structures find extensive application in civil engineering projects, including buildings, bridges, and towers, due to their dependable material characteristics, expeditious construction capabilities, and cost-efficiency. In such structures, beams and columns are interconnected through either welding or bolting methods. However, it's imperative to recognize that joints represent the critical areas susceptible to damage stemming from a variety of factors, both human-induced and environmental, in addition to the effects of aging. Over the past few decades, substantial attention has been dedicated to the field of Structural Health Monitoring (SHM) at the joints of steel structures. This study seeks to comprehensively evaluate various methods employed for structural health monitoring at the joints of steel structures, encompassing both bolted and welded connections. While there have been numerous prior review studies that focus on localized and vibration-based techniques for detecting damage at these joints, there is a conspicuous absence of research covering the amalgamation of localized and global approaches across diverse steel structure types. This review paper addresses this gap by offering a thorough examination, incorporating the most recent applications of SHM methodologies employed in research and practical contexts for joint damage detection. Furthermore, it serves as a valuable resource for professionals, engineers, and academics engaged in civil structure design, construction, and maintenance.

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“…A special case is the monitoring of underground storage tanks. A recent review identified ultrasonic guided waves and AE, and specifically a fusion of signals from both methods, as the most promising approach [205]. Advanced signal analysis (Support Vector Machine) is discussed for a water storage tank by [206].…”

Section: Structural Componentsmentioning

confidence: 99%

AE in Polymeric Composites

Sause

Brunner

2021

Springer Tracts in Civil Engineering

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Polymeric composites comprise a wide range of materials consisting of continuous or discontinuous fibers, various particles, or combinations of these embedded in a polymer matrix. Beside technical polymer composites, biobased composites with biopolymers or natural fibers, or natural polymer composites such as wood are finding increasing use in structural applications. The complex, multi-scale morphology yields distinctly different mechanisms generating AE under thermo-mechanical loads or environmental exposure. Storage tanks and pressure vessels made from fiber-reinforced composites were among the first components for which AE testing yielded reliable assessments of structural integrity. The empirical Felicity-ratio is important for quantitative predictions of structural damage and remaining service life. Recent advances in AE signal analysis now contribute to improved source location accuracy and to the unambiguous identification of the underlying microscopic signal source mechanisms. AE testing of infrastructure and components tends to move from periodic inspection to continuous structural health or condition monitoring. This also applies to infrastructure made from polymeric composites as well as to structures or parts in the transportation industry. AE implemented for process monitoring related to polymeric composites shows potential for development of AEbased process control. This chapter first reviews the mechanisms generating AE in polymeric composites, then discusses progress in AE signal analysis for source location and identification of mechanisms and presents selected examples of established AE applications from the micro-to the macro-scale. This includes prediction and quantification of damage in materials and structures, and closes with prospects for developments of AE condition monitoring and process control.

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Review of Underground Storage Tank Condition Monitoring Techniques

Cited by 9 publications

References 98 publications

Design an Internet of Things Simulator of Fluid Volume Monitoring System for Petrol Station Underground Tank

Design an Internet of Things Simulator of Fluid Volume Monitoring System for Petrol Station Underground Tank

A comprehensive review on health monitoring of joints in steel structures

AE in Polymeric Composites

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