Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties

The scouring phenomenon is one of the major problems experienced in hydraulic engineering.In this study,an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches,including the ant colony optimization,genetic algorithm,teaching-learning-based optimization,biogeographical-based optimization,and invasive weed optimization for estimating the long contraction scour depth.The proposed hybrid models are built using non-dimensional information collected from previous studies.The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations.Besides,the uncertainty of models,variables,and data are inspected.Based on the achieved modeling results,adaptive neuro-fuzzy inference system-biogeographic based optimization(ANFIS-BBO)provides superior prediction accuracy compared to others,with a maximum correlation coefficient(R_(test)=0.923)and minimum root mean square error value(RMSE_(test)=0.0193).Thus,the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring,thus,contributing to the base knowledge of hydraulic structure sustainability.

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.