scholarly journals Expert Panel, Preventive Maintenance of Heritage Buildings and Fuzzy Logic SYSTEM: An Application in Valdivia, Chile

2021 ◽  
Vol 13 (12) ◽  
pp. 6922
Author(s):  
Manuel Carpio ◽  
Andrés J. Prieto
Keyword(s):  
Fuzzy Logic ◽  
Human Error ◽  
Expert Panel ◽  
Fuzzy Model ◽  
Lessons Learned ◽  
Digital Tool ◽  
Building Maintenance ◽  
Large Dispersion ◽  
Input Parameters ◽  
Heritage Buildings

The maintenance of buildings is a highly complex decision process, which is generally due to professional experts having to consider several arduous evaluations, especially regarding uncertainty related to why, when and how to intervene. This study concerns the analysis of the uncertainty associated with professional experts’ surveys during the decision-making process during building maintenance. For this purpose, a case study of a timber-structure building was examined. An expert panel of 66 professionals with expertise in construction engineering carried out a systematic and automated evaluation. This kind of digital method is capable of managing the uncertainty associated with the evaluation processes by different specialists. Experts can evaluate various nuances and approximations in the model’s input parameters. The fuzzy model helps to harmonize the results since minor variations in the evaluation of the input parameters do not generate a large dispersion over the model’s output variable. The novelty of this study concerns the application of a digital methodology based on a fuzzy logic model to assist a professional expert panel in different areas—architecture, engineering and construction. This study is oriented through an artificial intelligence based method applied by specialists to set intervention priorities, support maintenance management of the examined building and minimise human error during data collection and uncertainty related to making decisions. The lessons learned from the results obtained in this study promote the use of this kind of digital tool to manage the uncertainty associated with in-situ visual inspections.

Evaluation of significant transitions in the influencing factors of human reliability

2008 ◽  
Vol 222 (1) ◽  
pp. 39-45 ◽  
Author(s):  
M Konstandinidou ◽  
Z Nivolianitou ◽  
C Kiranoudis ◽  
N Markatos
Keyword(s):  
Human Error ◽  
Fuzzy Model ◽  
Cost Benefit ◽  
Human Operator ◽  
Adjustment Cost ◽  
Human Reliability ◽  
Input Parameters ◽  
Fuzzy Logic Theory ◽  
Error Probabilities ◽  
The Impact

This paper describes sensitivity analysis of a previously developed model for the estimation of the probability of human erroneous actions in specific industrial and working contexts. The model has been built using the fuzzy logic theory. The fuzzy model developed includes nine input parameters according to the CREAM (cognitive reliability and error analysis method) methodology. The model has been used in order to detect the critical transitions in the optimization of human reliability, for corresponding changes in each of the nine input parameters. Changes in the working context have been evaluated through their relevant reduction to the action failure probability of the human operator. The results of the fuzzy model, after the defuzzification process, are human error probabilities in the form of crisp numbers, which can eventually be used in probabilistic assessments and cost—benefit analyses in order to compare the parameters’ adjustment cost with the impact this adjustment has on the performance and reliability of the human operator.

scholarly journals EXPERT PANEL ON IN-SITU VISUAL INSPECTIONS FOR MASONRY CHURCHES MAINTENANCE STAGE

2021 ◽  
Vol 27 (6) ◽  
pp. 454-471
Author(s):  
Manuel Carpio ◽  
Jesús Ortega ◽  
Andrés J. Prieto
Keyword(s):  
Preventive Maintenance ◽  
Human Error ◽  
Expert Panel ◽  
High Dispersion ◽  
Preventive Conservation ◽  
Heritage Buildings ◽  
Conservation Actions ◽  
Input Variables ◽  
Masonry Churches

The incorporation of protocols in heritage building preservation is important for the definition of preventive conservation actions. Such integration is needed to avoid restoration actions and to promote preventive maintenance instead of corrective maintenance actions. This paper presents the application of an innovative digital management system using artificial intelligence that can quantify the suitability of a sample. This kind of application can support the maintenance management of buildings and minimise human error in data collection. The fuzzy system showed slight differences between the members of the expert panel during the in-situ visual inspection. These results indicate that, despite differences between various experts’ evaluation of a building, the proposed digital method helps minimise the uncertainty in the results. The paper highlights input variables, which present high dispersion (load state modification, fire and occupancy), and input parameters, which present low dispersion (preservation, roof design and overloads). Fuzzy systems can adequately manage the uncertainties associated with different experts’ assessment of sample that present constructive homogeneity. This study can give advantages to stakeholders during the inspection, diagnosis and evaluation stages in the improvement of mitigation policies focused on preventive maintenance programs dedicated to the resilience of heritage buildings, specifically churches emplaced in Chile.

A Fuzzy Logic Based Model to Predict Weld Width – A Case Study of Hard Facing Process Using MIG Welding on Dual Plate Check Valve

2014 ◽  
Vol 592-594 ◽  
pp. 8-12 ◽  
Author(s):  
Saurin Sheth ◽  
Bhavin S. Modi ◽  
Tejas Patel ◽  
P.M. George
Keyword(s):  
Fuzzy Logic ◽  
Gas Flow ◽  
Fuzzy Model ◽  
Welding Process ◽  
Check Valve ◽  
Bead Geometry ◽  
Mig Welding ◽  
Weld Bead Geometry ◽  
Weld Width ◽  
Input Parameters

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of weld-bead geometry. The 23 replication with 4 center points experiments are performed on non-return valve material WCB by varying various MIG Welding process parameters. Here welding current, welding speed and gas flow rate are considered as input parameters with two levels. A fuzzy model is developed to predict the weld width in context of these input parameters. Fuzzy model uses fuzzy expert rules, triangular membership function and centroid area method for defuzzyfication process using MATLAB fuzzy logic tool box. The developed model is validated by performing experiments at center points. The result shows prediction may lie in the range of 95.18-100%.

scholarly journals Fuzzy logic simulation of oat yield after using hydrogel and nitrogen biopolymer management

2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Biomass Yield ◽  
New Technologies ◽  
Block Design ◽  
Fuzzy Model ◽  
Logic Simulation ◽  
Efficient Tool ◽  
Randomized Block Design ◽  
Maximum Air Temperature

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing

Biometrics ◽  
2017 ◽  
pp. 907-932 ◽  
Author(s):  
Niladri Sekhar Datta ◽  
Himadri Sekhar Dutta ◽  
Koushik Majumder
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Fuzzy Variables ◽  
Truth Value

Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem and analysis of the Fuzzy information where the Fuzzy optimization solves Fuzzy model optimally using optimization techniques via membership functions. In this research article authors describe the Fuzzy rules and its application and the different types of well known problems solved by the Fuzzy optimization technique.

Research of Screening and Grading Method of Ageing Sensitive Objectives in Nuclear Power Plant

2014 ◽  
Vol 543-547 ◽  
pp. 858-861
Author(s):  
Xiao Tian Liu ◽  
Yong Wang ◽  
Shao Rui Niu ◽  
Yan Zhao Zhang ◽  
Zhen Hao Shi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Lessons Learned ◽  
Nonlinear Coupling ◽  
Highly Nonlinear ◽  
Ageing Management ◽  
First Time

This first step of ageing management in nuclear power plant is to determine the objectives and their priorities. The characteristics of the objectives are complex and highly nonlinear coupling. A fuzzy logic based screening and grading method have been developed in this research for the first time which combined the genetic ageing lessons learned and field expert experience to resolve the problem. The method have been approved of highly applicability and applied to ageing management in multiple nuclear power plants.

Prediction of Solar Photovoltaic/Thermal Collector Power Output Using Fuzzy Logic

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
M. Sridharan ◽  
G. Jayaprakash ◽  
M. Chandrasekar ◽  
P. Vigneshwar ◽  
S. Paramaguru ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Power Output ◽  
Thermal Power ◽  
Fuzzy Model ◽  
Experimental Tests ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Input Process ◽  
Hybrid Devices ◽  
Flat Plate Collector

In recent years, solar PV/T water collectors have been identified as one of the most promising hybrid devices. It is a combination of solar photovoltaic (PV) and solar flat plate collector (FPC) systems capable of generating electrical and thermal power simultaneously. This study presents a model which predicts solar PV/T collector power output using fuzzy logic techniques. A fuzzy logic model was established to predict power output of PV/T with respect to changes in input process and FPC output power. Membership functions were allocated in connection with each model input. Experimental tests conducted during the month of December 2016 are compared with the developed fuzzy model to verify predicted results. The results indicate an agreement between fuzzy model and experimental results with an accuracy of 94.38% and error of 5.62%.

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