A fuzzy logic approach to predict seismic ductility and shear strength of reinforced concrete elements

2010 ◽  
Vol 37 (9) ◽  
pp. 1232-1246
Author(s):  
Abdelsamie Elmenshawi ◽  
Tom Brown ◽  
Nigel Shrive
Keyword(s):  
Shear Strength ◽  
Fuzzy Inference ◽  
Beam Width ◽  
Reinforcement Ratio ◽  
Design Parameters ◽  
Lateral Shear ◽  
Inference System ◽  
Displacement Ductility ◽  
Depth Ratio ◽  
Proposed Model

Structures require ductility to withstand severe earthquake-induced loads and remain standing. A new method for modelling seismic displacement ductility is proposed here, in which a fuzzy inference system is utilized to include the uncertainty in the parameters that influence this behaviour. The proposed model is also used to determine the lateral shear strength, a vital parameter in seismic design. Experimental data are presented for beams subjected to cyclic loading. Numerous input design parameters were considered including the beam width/depth ratio, the longitudinal reinforcement ratio, the bottom/top reinforcement ratio, the concrete compressive strength, the transverse reinforcement strength, and the shear span-to-depth ratio. Output parameters included the displacement ductility and lateral shear strength. The proposed model can predict the outputs successfully with an error of ±20%, but is more effective in predicting shear strength than displacement ductility.

scholarly journals Shear strength of fibre reinforced polymers (FRP) used as internal reinforcement for reinforced concrete (RC) beams

2021 ◽  
Vol 19 (2) ◽  
pp. 207-218
Author(s):  
Milos Milovancevic
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Fuzzy Inference ◽  
Selection Procedure ◽  
Beam Width ◽  
Strength Prediction ◽  
Strength Analysis ◽  
Rc Beams ◽  
Reinforced Polymers ◽  
Inference System

The main aim of the study was to perform selection procedure in order to find the optimal predictors for the shear strength of fibre reinforced polymers (FRP) used as internal reinforcement for reinforced concrete (RC) beams. The procedure was performed by adaptive neuro fuzzy inference system (ANFIS) and all available parameters are included. The ANFIS model could be used as simplification of the shear strength analysis of the FRP-RC beams. MATLAB software was used for the ANFIS application for the shear strength prediction of the FRP-RC beams. The results from the searching procedure indicated that ?beam width? and ?effective depth? form the optimal combination of two input attributes or two predictors for the shear strength prediction of the FRP-RC beams. This selected two predictors could be used effectively to estimate the strength of the FRP-RC beams.

Determining the safety culture in a gun factory in Turkey: A fuzzy approach

2020 ◽  
pp. 1-11
Author(s):  
Gökçen A. Çiftçioğlu ◽  
Mehmet A. N. Kadırgan ◽  
Ahmet Eşiyok
Keyword(s):  
Safety Culture ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Self Awareness ◽  
Inference System ◽  
Five Factors ◽  
Initial Questionnaire ◽  
Proposed Model ◽  
Assessment Questionnaire

Safety culture is a very complex phenomenon due to its intangible nature. It is tough to measure and express it with numerical values, as there is no simple indicator to measure it. This paper presents a fuzzy inference system that measures the safety culture. First of all, a safety culture assessment questionnaire is developed by utilizing related literature. The initial questionnaire had 29 items. The questionnaire is applied to 259 employees within the gun manufacturing factory. After making an exploratory factor analysis, the questionnaire is based on five factors with 25 items. The safety culture indicators are defined as; safety follow-up audit reporting, employees’ self-awareness, operational safety commitment, management’s safety commitment, safety orientedness. Normality, reliability, and correlation analysis are performed. Then a fuzzy model is constructed with five inputs and one output. The inputs are the five factors mentioned above, and the output generated is the safety culture result, which is between 0-1. The presented fuzzy model produces reliable results indicating the safety culture level from the employees’ eyes. Beyond exploring the employees’ safety culture, the proposed model can easily be understood by the practitioners from various sectors. Furthermore, the model is straightforward to customize for various fields of industry.

scholarly journals PROPOSING A NEW METHODOLOGY BASED ON FUZZY LOGIC FOR TUNNELLING RISK ASSESSMENT

2014 ◽  
Vol 20 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference ◽  
Assessment Model ◽  
Tunnel Construction ◽  
Construction Operations ◽  
Protective Measures ◽  
Inference System ◽  
Proposed Model ◽  
Mine Development ◽  
Civil Defence

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
İlker Gölcük
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Real Life ◽  
Effect Analysis ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
Interval Type

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

scholarly journals Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

2019 ◽  
Vol 20 (1) ◽  
pp. 148-156
Author(s):  
Seyed Hesam Alihosseini ◽  
Ali Torabian ◽  
Farzam Babaei Semiromi
Keyword(s):  
Fuzzy Inference System ◽  
Quality Index ◽  
Municipal Wastewater ◽  
Fuzzy Inference ◽  
Treatment Plant ◽  
Wastewater Effluent ◽  
Inference System ◽  
Effluent Quality ◽  
Proposed Model ◽  
Treated Municipal Wastewater

Abstract The issues of freshwater scarcity in arid and semi-arid areas could be reduced via treated municipal wastewater effluent (TMWE). Artificial intelligence methods, especially the fuzzy inference system, have proven their ability in TMWE quality evaluation in complex and uncertain systems. The primary aim of this study was to use a Mamdani fuzzy inference system to present an index for agricultural application based on the Iranian water quality index (IWQI). Since the uncertainties were disregarded in the conventional IWQI, the present study improved this procedure by using fuzzy logic and then the fuzzy effluent quality index (FEQI) was proposed as a hybrid fuzzy-based index. TMWE samples of the Gheitarie wastewater treatment plant in Tehran city recorded from 2011 to 2017 were taken into consideration for testing the ability of the proposed index. The results of the FEQI showed samples categorized as ‘Excellent’ (21), ‘Good’ (10), ‘Fair’ (4), and ‘Marginal’ (1) for the warm seasons, and for the cool seasons, the samples categorized as ‘Excellent’, ‘Good’ and ‘Fair’ were 17, 18 and 1, respectively. Generally, a comparison between the IWQI and proposed model results revealed the FEQI's superiority in TMWE quality assessment.

Equipment Support Training Level Evaluation Method based on ANFIS

2013 ◽  
Vol 706-708 ◽  
pp. 1950-1953
Author(s):  
Wu Kui Zhao ◽  
Cheng Zhang ◽  
Yi Bo Wang
Keyword(s):  
Evaluation Method ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Back Propagation ◽  
Training Data ◽  
Training Level ◽  
Inference System ◽  
Proposed Model ◽  
Training Efficiency

The evaluation of equipment support training is an effective way to improve training efficiency. The main influencing factors of equipment support training are analyzed. Adaptive neural fuzzy inference system (ANFIS) model structure is established and the hybrid-learning algorithm to solve the established model by applying back-propagation and least mean squares procedure is investigated. Then the evaluation model of equipment support training level based on ANFIS is constructed. The training level consistent with the actual training level is achieved by training the proposed model using training data samples, which verifies the correctness and effectiveness of the proposed method. Simulation comparing analysis using the proposed method and BP neutral network is conducted respectively. The superiority of the proposed method is verified by simulation results, which provides an effective method for equipment support training evaluation.

Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system

2009 ◽  
Vol 223 (10) ◽  
pp. 2369-2381 ◽  
Author(s):  
Sivarao Subramonian ◽  
P Brevern ◽  
N S M El-Tayeb ◽  
V C Vengkatesh
Keyword(s):  
Surface Roughness ◽  
Pressure Vessel ◽  
Fuzzy Inference System ◽  
Laser Processing ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Adaptive Network ◽  
Inference System ◽  
Cut Quality

Real-world problems in precision machining now require intelligent systems that integrate knowledge, techniques, and methodologies. Intelligent systems possess human-like expertise within a specific domain to adapt themselves and to learn to do better in making decisions for an intelligent manufacturing system. An intelligent tool called adaptive network-based fuzzy inference system (ANFIS) was used to model and predict the laser cut quality of a 2.5 mm manganese—molybdenum (Mn—Mo) alloy pressure vessel plate in this article. A 3 kW CO2 laser machine with seven selected design parameters was used to carry out 128 experiments based on 2 k factorial design with single replication. Because surface roughness (Ra) was the response parameter, it was targeted to be <15 μm to meet the requirement and benchmark of the pressure vessel manufacturer who sponsored this project. The DIN 2310-5 German laser cutting of metallic materials standard and procedure was referred to for evaluating surface roughness, where experimentally obtained results were used for Ra predictive modelling. Predictions of non-linear laser processing by ANFIS were found to be extremely promising in supplying the desired output, where Ra was predicted to an excellent degree of accuracy, reaching almost 70 per cent with the experimental pure error below 30 per cent.

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