Cyber-physical systems for temporary structure monitoring

Yuan, Xin; Anumba, Chimay J.; Parfitt, M. Kevin

doi:10.1016/j.autcon.2016.02.005

Cited by 104 publications

(44 citation statements)

References 10 publications

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“…Nonetheless, automation of construction processes or sensor embeddedness in structures is far from ubiquitous. Use of sensors within structures are limited to specific (though numerous) cases of highly controlled and rigorous experiences of Structural Health Monitoring . It can be stated that the massive gathering of data coming from sensors in structures is still in its infancy.…”

Section: Review Of the Earlier Workmentioning

confidence: 99%

Development of IoT applications in civil engineering classrooms using mobile devices

Chacón¹,

Posada²,

Toledo³

et al. 2018

Comp Applic In Engineering

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This paper presents academic efforts aimed at integrating methodologies associated with the use of mobile devices, the potential of the Internet of Things (IoT), and the role of experimental education in civil engineering. This integration is developed by encompassing the use of sensors, microcontrollers, civil engineering problems, app development, and fabrication. The proposal provides an explorative way of approaching the numerous possibilities that arise in civil engineering when it comes to IoT, automation, monitoring, and control of civil engineering processes. The used tools represent accessible and affordable ways for application in classrooms and in educational laboratories for beginners. The initial explorative approach implies the fusion of three realms: (i) the phenomenology and mathematics of varied civil engineering problems; (ii) the systematic use of digital fabrication technologies and electronic prototyping platforms; and (iii) the creative and visual way of developing codes provided by block‐based development platforms. This integration of perspectives is an attempt of approaching civil engineering mathematics to technology and arts with a rigorous scientific approach. A set of different examples is presented with the corresponding findings in educational terms. These examples are developed in a constructive, scaffolding‐based way and may contribute as a potential alternative in the development of open‐source teaching labs in civil engineering schools.

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Section: Review Of the Earlier Workmentioning

confidence: 99%

Development of IoT applications in civil engineering classrooms using mobile devices

Chacón¹,

Posada²,

Toledo³

et al. 2018

Comp Applic In Engineering

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“…To assist in the monitoring of safety problems during construction, a few research studies have sought real-time monitoring approaches [10][11][12][13]. Xue et al [10] proposed the use of radio frequency identification (RFID), together with scaffold safety management, in order to warn safety managers in real-time.…”

Section: Introductionmentioning

confidence: 99%

“…Xue et al [10] proposed the use of radio frequency identification (RFID), together with scaffold safety management, in order to warn safety managers in real-time. Yuan et al [11] developed a cyber-physical system, which integrates the temporary structure with its virtual model in order to automate potential hazard detection. Jung et al [12] explored the capability of sequential image processing to automate the possible failure detection of temporary structures.…”

Section: Introductionmentioning

confidence: 99%

“…Cho et al [13] demonstrated the applicability of machine learning (ML) to automate the classification of scaffold safety conditions, using the strain measurements of the scaffold members. However, these efforts to automate scaffold safety assessments are based on a limited number of safety conditions, which are significantly simpler with respect to the mode of failure corresponding to scaffolds used at most construction sites [11][12][13]. As past investigations have identified hazards by checking against threshold values [11], or accounting for only a small number of failure cases [11][12][13] associated with structural configuration, there may be difficulty in establishing their applicability for generic, temporary structures and addressing their potential risks.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety

Sakhakarmi

Park

2020

IJERPH

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A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety prediction for scaffolds. It implements a divide-and-conquer technique with deep learning. As a test scaffolding, a four-bay, three-story scaffold model was used. Analysis of the model led to 1411 unique safety cases for the model. To apply deep learning, a test simulation generated 1,540,000 datasets for pre-training, and an additional 141,100 datasets for testing purposes. The cases were then sub-divided into 18 categories based on failure modes at both global and local levels, along with a combination of member failures. Accordingly, the divide-and-conquer technique was applied to the 18 categories, each of which were pre-trained by a neural network. For the test datasets, the overall accuracy was 99%. The prediction model showed that 82.78% of the 1411 safety cases showed 100% accuracy for the test datasets, which contributed to the high accuracy. In addition, the higher values of precision, recall, and F1 score for the majority of the safety cases indicate good performance of the model, and a significant improvement compared with past research conducted on simpler cases. Specifically, the method demonstrated improved performance with respect to accuracy and the number of classifications. Thus, the results suggest that the methodology could be reliably applied for the safety assessment of scaffolding systems that are more complex than systems tested in past studies. Furthermore, the implemented methodology can easily be replicated for other classification problems.

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“…Yuan et al [16] propose a set of Cyber-Physical Systems (CPS) to prevent failure of temporary structures through realtime monitoring. Their solution is supported with a mobile application and a cloud-based database.…”

Section: Related Work -Smart Construction Application Opportunitiesmentioning

confidence: 99%

Supporting smart construction with dependable edge computing infrastructures and applications

Kochovski

Stankovski

2018

Automation in Construction

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The Internet of Things (IoT) such as the use of robots, sensors, actuators, electronic signalization and a variety of other Internet enabled physical devices may provide for new advanced smart applications to be used in construction in very near future. Such applications require real-time responses and are therefore time-critical. Therefore, in order to support collaboration, control, monitoring, supply management, safety and other construction processes, they have to meet dependability requirements, including requirements for high Quality of Service (QoS). Dependability and high QoS can be achieved by using adequate number and quality of computing resources, such as processing, memory and networking elements, geographically close to the smart environments. The goal of this study is to develop a practical edge computing architecture and design, which can be used in to support smart construction environments with high QoS. This study gives particular attention to the solution design, which relies on latest cloud and software engineering approaches and technologies, and provides elasticity, interoperability and adaptation to companies' specific needs. Two edge computing applications supporting video communications and construction process documentation are developed and demonstrate a viable edge computing design for smart construction.

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Cyber-physical systems for temporary structure monitoring

Cited by 104 publications

References 10 publications

Development of IoT applications in civil engineering classrooms using mobile devices

Development of IoT applications in civil engineering classrooms using mobile devices

Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety

Supporting smart construction with dependable edge computing infrastructures and applications

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