An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects

Martínez-Rojas, María; Gacto, María José; Vitiello, Autilia; Soto-Hidalgo, J. M.

doi:10.3390/s21196461

Cited by 11 publications

(9 citation statements)

References 87 publications

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“…Therefore, integrating technology and safety knowledge is crucial, including real-time decision-making capabilities and knowledge management about risk. In this study, Martinez-Rojas et al 5 built an IoT infrastructure integrated with open source libraries to support expert decision-making. Finally, Lestari et al 40 developed a framework to evaluate the safe environment and improve construction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The same dataset contains the construction details: the total construction cost of the project, construction period, construction site location, date of the accident, amount of loss, and detailed loss history. The number of floors of www.nature.com/scientificreports/ a building has a contribution in increasing the risk of falling objects; as the height increases the use of scaffolding and cranes is unavoidable which induces the risk of falling objects 5 .…”

Section: Data Descriptionmentioning

confidence: 99%

“…For instance, the safety accident rate at construction sites is cumulative own to the use of heavy equipment and work at heights due to increasing size and height of buildings. In addition, there is a high risk of third-party damage to nearby buildings and pedestrians due to falling objects at building construction sites in the city center 5 . Noise and vibrations generated from construction sites are also causing problems.…”

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confidence: 99%

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Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

Kim

Bae

Park

et al. 2022

Sci Rep

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This study aims to generate a deep learning algorithm-based model for quantitative prediction of financial losses due to accidents occurring at apartment construction sites. Recently, the construction of apartment buildings is rapidly increasing to solve housing shortage caused by increasing urban density. However, high-rise and large-scale construction projects are increasing the frequency and severity of accidents occurring inside and outside of construction sites, leading to increases of financial losses. In particular, the increase in severe weather and the surge in abnormal weather events due to climate change are aggravating the risk of financial losses associated with accidents occurring at construction sites. Therefore, for sustainable and efficient management of construction projects, a loss prediction model that prevents and reduces the risk of financial loss is essential. This study collected and analyzed insurance claim payout data from a main insurance company in South Korea regarding accidents occurring inside and outside of construction sites. Deep learning algorithms were applied to develop predictive models reflecting scientific and recent technologies. Results and framework of this study provide critical guidance on financial loss management necessary for sustainable and efficacious construction project management. They can be used as a reference for various other construction project management studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

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Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

Kim

Bae

Park

et al. 2022

Sci Rep

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“…Nevertheless, the interaction between professionals from different disciplines, companies, and countries generates a heterogeneous environment affecting accident rates [ 18 , 19 , 20 ]. Moreover, indicators such as working with heavy equipment, working at heights, falling objects, noise, and vibrations act cumulative to the increase of frequency and severity of accidents on construction sites [ 17 , 21 ]. Furthermore, previous research has highlighted that geographical disadvantages such as working in mountainous and river regions significantly affect the possibility of accidents on construction sites [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Work-Related Injuries among Insured Construction Workers Presenting to a Swiss Adult Emergency Department: A Retrospective Study (2016–2020)

Dethlefsen¹,

Orlik²,

Mûller

et al. 2022

IJERPH

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Occupational injuries are one of the main causes of Emergency Department visits and represent a substantial source of disability or even death. However, the published studies and reports on construction–occupational accidents in Switzerland are limited. We aimed to investigate the epidemiology of fatal and non-fatal injuries among construction workers older than 16 years of age over a 5-year period. Data were gathered from the emergency department (ED) of Bern University Hospital. A retrospective design was chosen to allow analysis of changes in construction accidents between 2016–2020. A total of 397 patients were enrolled. Compared to studies in other countries, we also showed that the upper extremity and falling from height is the most common injured body part and mechanism of injury. Furthermore, we were able to show that the most common age group representing was 26–35 years and the second common body part injured was the head, which is a difference from studies in other countries. Wound lacerations were the most common type of injury, followed by joint distortions. By stratifying according to the season, occupational injuries among construction workers were found to be significant higher during summer and autumn. As work-related injuries among construction workers are becoming more common, prevention strategies and safety instructions must be optimized.

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“…Under this architecture, FLSs are transformed into services that are globally accessible, thereby enabling transparency, system neutrality, load distribution, resource allocation, and cost efficiency. Also, such a web-based solution needs a webbased data language for exchanging information about FLSs [6]. A recently developed standard for this purpose is the IEEE-1855 (2016)-also known as Fuzzy Markup Language (FML) [7].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

Pandya

Pourabdollah

Lotfi

2022

Int. J. Fuzzy Syst.

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Traditionally, fuzzy logic systems are linked to specific hardware or software systems. Observations reveal that dispersed and distributed designs of intelligent systems are gaining attraction. Due to the possible complexities of fuzzy logic computations, distributed architectures have the potential to add value to the development of fuzzy systems. However, the absence of best practices and standard methodologies may prevent widespread adoption. By broadening the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building a Service-Oriented Architecture (SOA) as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. A comparison between the performances of a stand-alone hardware-dependent solution and a cloud-based solution (known as fuzzy-as-a-service) is performed. The analysis is also carried out on two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library). The analysis and evaluation are performed on a human fall detection scenario involving wearable sensors. The proposed algorithm can identify between fall and non-fall events. However, the results show that the processing time taken per 10,000 samples using smartwatch and mobile was 2220 s and 101 s for a cloud-based non-fuzzy machine learning system, 1111 s and 45 s for a cloud-based fuzzy system with AWS and JFML, and 1250 s and 97 s for a cloud-based fuzzy system with Microsoft Azure and Simpful libraries. It has been observed that a smartwatch with a fuzzy stand-alone crashed after processing 5000 samples and a mobile phone requires 179.42 s to process 10,000 samples.

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An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects

Cited by 11 publications

References 87 publications

Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

Work-Related Injuries among Insured Construction Workers Presenting to a Swiss Adult Emergency Department: A Retrospective Study (2016–2020)

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

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