Moment capacity estimation of spirally reinforced concrete columns using ANFIS

Naderpour, Hosein; Mirrashid, Masoomeh

doi:10.1007/s40747-019-00118-2

Cited by 29 publications

(14 citation statements)

References 32 publications

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“…With the above in mind, Equation (1) illustrates the final Risk Magnitude (RM):

where

, and

represent probability, severity, exposure, and detectability, accordingly. It is essential to mention that the input data (P, S, E, and D) and output data (RM) were normalized to simplify the procedure using Equation (2), which has been used by many researchers [ 57 , 58 ].

…”

Section: Methodsmentioning

confidence: 99%

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Sadeghi

Mohandes

Hosseini

et al. 2020

IJERPH

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Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

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“…With the above in mind, Equation (1) illustrates the final Risk Magnitude (RM):

where

, and

…”

Section: Methodsmentioning

confidence: 99%

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Sadeghi

Mohandes

Hosseini

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…The human brain, as a complex natural system, is unique in its kind. Some of the processes in this natural system are so complex that their processing is also complex for many super systems [39][40][41][42][43][44]. Analytical processes are very complex, because of the high speed and power of information processing by brain cells.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Haghshenas

Pirouz

et al. 2020

IJERPH

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Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

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“…Haven determined the electrode configurations, the placement of the sensors was preceded by palpation of the remaining arm muscles in the amputee subjects to locate their belly and length as indicated in previous studies [24,25]. Afterwards, the sensors were placed over the skin area underlying the identified arm muscles in a symmetrical manner across both arms with the aid of adhesive (Figure 2a).…”

Section: Emg Data Measurementmentioning

confidence: 99%

“…In summary, the outcomes of this study are capable of providing researchers and developers with proper insight on how to best select features and/or windowing parameters to achieve optimal movement intent decoding in EMG pattern recognition systems. Furthermore, it may spur potential advancement in smart prosthetic control system and other areas that employs pattern recognition based concept for the provision of smart healthcare services [22][23][24].…”

mentioning

confidence: 99%

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

et al. 2020

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The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

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Moment capacity estimation of spirally reinforced concrete columns using ANFIS

Cited by 29 publications

References 32 publications

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

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