Sensors for Structural Health Monitoring of Agricultural Structures

Maraveas, Chrysanthos; Bartzanas, Thomas

doi:10.3390/s21010314

Cited by 24 publications

(15 citation statements)

References 98 publications

(225 reference statements)

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“…The overall findings from the SLR related to the identified needs are presented in Appendix A Table A2, with a sample of the findings in Table 7. Regarding the barriers discussed in the 51 articles (of which, Table 8 presents a sample-with the full list of barriers in Appendix A Table A3), the categories of Ecology and Health (16), Policy (16), ICT (16) and Socio-Economic (19) had almost equally prominent representation, with a similar number of barriers identified for Infrastructure (12). Much of the ICT barriers were related to articles discussing the issues surrounding IoT (integration, governance, cost, compatibility, etc.…”

Section: (Rq3) What Are the Main Barriers Or Needs And The Resulting ...mentioning

confidence: 99%

“…Going forwards, a synergy should be established between (i) upgraded infrastructure and (ii) a reduction in waste behaviours (i.e, excess) to cater for strain and the future demands placed by urbanisation. Relating to point (i), suitable technologies discussed in the literature include integrating AI/machine learning techniques [15], IoT sensors [16], digital twinning technologies [17,18], smart grid [19] and solar and wind [20]. Regarding point (ii), as discussed later in the findings, a suitable approach for eliminating wastegenerating behaviour is through education and developing an awareness of the impact waste behaviour has on the availability of critical services.…”

Section: Introductionmentioning

confidence: 99%

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Critical Infrastructures: Reliability, Resilience and Wastage

et al. 2022

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By 2050, according to the UN medium forecast, 68.6% of the world’s population will live in cities. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order to create a sustainable society, there needs to be a change in both societal behaviours (for example, reducing water, energy or food waste activities) and future use of smart technologies. The main challenges are that there is a limited aggregated understanding of current waste behaviours within critical infrastructure ecosystems, and a lack of technological solutions to address this. Therefore, this article reflects on theoretical and applied works concerning waste behaviours, the reliability/availability and resilience of critical infrastructures, and the use of advanced technologies for reducing waste. Articles in the Scopus digital library are considered in the investigation, with 51 papers selected by means of a systematic literature review, from which 38 strains, 86 barriers and 87 needs are identified, along with 60 methods of analysis. The focus of the work is primarily on behaviours, barriers and needs that create an excess or wastage.

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Section: (Rq3) What Are the Main Barriers Or Needs And The Resulting ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Critical Infrastructures: Reliability, Resilience and Wastage

et al. 2022

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“…Presently, there is a wide array of IoT-based sensors for smart greenhouses, including plant growth sensors, temperature and humidity sensors, insect detection sensors, soil temperature, pH, and moisture sensors, and solar radiation, atmospheric pressure, wind speed, and CO 2 (and other gas) sensors, which rely on Bragg, piezoelectric, electrochemical, electromagnetic [31], and fiber-optic technologies for accurate assessment of the desired parameters [47] (see Table 1). The parameters of interest include different wavelengths of light, photocurrent, fluorescence intensity, the fluorescent signal emitted by plant chlorophyll, optical density, and the electrochemical signal generated by enzyme-catalyzed redox reaction (SHA principle) [1].…”

Section: Iot-based Sensors For Smart Greenhousesmentioning

confidence: 99%

Application of Internet of Things (IoT) for Optimized Greenhouse Environments

Maraveas

Bartzanas

2021

AgriEngineering

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This review presents the state-of-the-art research on IoT systems for optimized greenhouse environments. The data were analyzed using descriptive and statistical methods to infer relationships between the Internet of Things (IoT), emerging technologies, precision agriculture, agriculture 4.0, and improvements in commercial farming. The discussion is situated in the broader context of IoT in mitigating the adverse effects of climate change and global warming in agriculture through the optimization of critical parameters such as temperature and humidity, intelligent data acquisition, rule-based control, and resolving the barriers to the commercial adoption of IoT systems in agriculture. The recent unexpected and severe weather events have contributed to low agricultural yields and losses; this is a challenge that can be resolved through technology-mediated precision agriculture. Advances in technology have over time contributed to the development of sensors for frost prevention, remote crop monitoring, fire hazard prevention, precise control of nutrients in soilless greenhouse cultivation, power autonomy through the use of solar energy, and intelligent feeding, shading, and lighting control to improve yields and reduce operational costs. However, particular challenges abound, including the limited uptake of smart technologies in commercial agriculture, price, and accuracy of the sensors. The barriers and challenges should help guide future Research & Development projects and commercial applications.

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“…Another research has studied the technological feasibility of autonomous corrosion assessment of reinforced concrete structures, in which the use of IoT and machine learning for autonomous corrosion condition assessment of RC structures were recommended (Taffese and Nigussie, 2020). Maraveas and Bartzanas (2021) have studied another type of structure through using various sensors for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, CI 22,3 piezoelectric, wireless, fiber Bragg grating sensors and self-sensing concrete. They confirmed the improvement of the functionality and accuracy of these sensors to assess the concrete structure through deployment of machine learning, deep learning and artificial intelligence in smart IoT-based agriculture.…”

Section: Deep Leaning and Internet Of Things To Deliver Smart Cities ...mentioning

confidence: 99%

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

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Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

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Sensors for Structural Health Monitoring of Agricultural Structures

Cited by 24 publications

References 98 publications

Critical Infrastructures: Reliability, Resilience and Wastage

Critical Infrastructures: Reliability, Resilience and Wastage

Application of Internet of Things (IoT) for Optimized Greenhouse Environments

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

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