Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method

Babaei, Morteza; Roozbahani, Abbas; Shahdany, Seied Mehdy Hashemy

doi:10.1007/s11269-018-2042-1

Cited by 31 publications

(5 citation statements)

References 25 publications

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“…Continuing on, fault-tree analysis (FTA) and event-tree analysis (ETA) are, according to Marhavilas et al (2014aMarhavilas et al ( , 2014b [43,44], well known DET-methods and valuable analytic tools worldwide, which have been used successfully by reliability experts in failure-analysis and for the reliability and safety of complex technical-systems, and also of OSH-systems. Nowadays, many studies continue incorporating FTAs/ETAs in OSH RAA, like: (i) The work of Mutlu et al (2019) [27], which depicts a OSH RAA approach in the textile industry by integrating FTA with Failure-Mode and Effect-Analysis (FMEA) and BIFPET, (ii) the articles of Babaei et al (2018) [45] and Yasli and Bolat (2018) [46], which include FTA in fuzzy OSH RAA methods, (iii) the paper of Gul and Ak (2018) [47], which illustrates the collaboration of MCDM with FTA, (iv) the work of Fuentes-Bargues et al (2017) [48], which combines Hazard and Operability (HAZOP) and FTA in risk analysis of fuel storage-terminals, and (v) the paper of Marhavilas et al (2014a) [43], which physically embody FTA in DET OSH-RAA techniques.…”

Section: Literature Surveymentioning

confidence: 99%

A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study

et al. 2020

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This study, on the one hand, develops a newfangled risk assessment and analysis (RAA) methodological approach (the MCDM-STO/DET one) for sustainable engineering projects by the amalgamation of a multicriteria decision-making (MCDM) process with the joint-collaboration of a deterministic (DET) and a stochastic (STO) process. On the other hand, proceeds to the application of MCDM-STO/DET at the workplaces of the Greek construction sector and also of the fixed-telecommunications technical projects of OTE SA (that is, the Greek Telecommunications Organization S.A.) by means of real accident data coming from two official State databases, namely of “SEPE” (Labor Inspectorate, Hellenic Ministry of Employment) and of “IKA” (Social Insurance Institution, Hellenic Ministry of Health), all the way through the period of the years2009–2016.Consequently, the article’s objectives are the following: (i) The implementation and execution of the joint MCDM-STO/DET framework, and (ii) to make known that the proposed MCDM-STO/DET algorithm can be a precious method for safety managers (and/or decision-makers) to ameliorate occupational safety and health (OSH) and to endorse the sustainable operation of technical or engineering projects as well. Mainly, we mingle two different configurations of the MCDM method, initially the Analytical Hierarchy-Process (the typical-AHP), and afterwards the Fuzzy-Extended AHP (the FEAHP) one, along with the Proportional Risk Assessment Technique (PRAT) and the analysis of Time-Series Processes (TSP), and finally with the Fault-Tree Analysis (FTA).

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Section: Literature Surveymentioning

confidence: 99%

A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study

et al. 2020

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“…Once the safety level for the foundry enterprise has been determined, corresponding safety measures should be adopted to protect employee health. Fault tree analysis [46,47], the traditional accident analysis method, can identify the causes of an accident, but cannot perform detailed analysis of identified risk factors. The cause and effect analysis method can identify the causes of the identified risk factors [36,37], and LOPA was applied to identify appropriate safety measures corresponding to the identified risk factors [10,38].…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health

Fang

2020

IJERPH

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Safety assessment of a casting workshop will provide a clearer understanding of the important safety level required for a foundry. The main purpose of this study was to construct a composite safety assessment method to protect employee health using the cloud model and cause and effect–Layer of Protection Analysis (LOPA). In this study, the weights of evaluation indicators were determined using the subjective analytic hierarchy process and objective entropy weight method respectively. Then, to obtain the preference coefficient of the integrated weight more precisely, a new algorithm was proposed based on the least square method. Next, the safety level of the casting workshop was presented based on the qualitative and quantitative analysis of the cloud model, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account; the validity of cloud model evaluation was validated by grey relational analysis. In addition, cause and effect was used to proactively identify factors that may lead to accidents. LOPA was used to correlate corresponding safety measures to the identified risk factors. 6 causes and 19 sub-causes that may contribute to accidents were identified, and 18 potential remedies, or independent protection layers (IPLs), were described as ways to protect employee health in foundry operations. A mechanical manufacturing business in Hunan, China was considered as a case study to demonstrate the applicability and benefits of the proposed safety assessment approach.

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“…Promotion of water consumption productivity in the agricultural sector, as the largest water consumer, has great importance in water use improvement (Babaei et al, 2018), leading to achieving the technical, economic, environmental and social objectives expected. Irrigation networks as a system for conveyance and distribution of water play a major role in this regard.…”

Section: Introductionmentioning

confidence: 99%

Irrigation canal control using enhanced fuzzy SARSA learning

Shahverdi

Monem

2022

Irrigation and Drainage

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Fuzzy SARSA learning (FSL) is a robust reinforcement learning (RL) technique that represents successful solutions in various industrial problems. Water management in irrigation canals is one of these problems where an FSL agent interacts with the canal environment to control the gates. FSL often requires a large number of interactive experiences and takes a long time in real‐life problems. To reduce the iteration and speed up the learning process, an enhanced FSL (EFSL) was developed to accelerate the process of policy learning. A MATLAB program was written, and combined with the irrigation canal conveyance system simulation (ICSS) model. To evaluate the proposed idea, the E1R1 Dez canal, located in the south‐west of Iran, was considered as a case study. Standard performance indicators were used for assessing the results based on considered water delivery scenarios, showing a shorter learning time with reasonable performance in controlling water depth changes within the canal.

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Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method

Cited by 31 publications

References 25 publications

A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study

A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study

Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health

Irrigation canal control using enhanced fuzzy SARSA learning

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