Concrete corrosion in wastewater systems:Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors.This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models.The models include three different types of extreme learning machines,including the standard,online sequential,and kernel extreme learning machines,in addition to the artificial neural network,classification and regression tree model,and statistical multiple linear regression model.The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models.The input variability was assessed based on two scenarios prior to the application of the predictive models.For the first assessment,the machine learning models were developed using all the available cement and concrete mixture input variables;the second assessment was conducted based on the gamma test approach,which is a sensitivity analysis technique.Subsequently,the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches.The adopted methodology attained optimistic and reliable modeling results.The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

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.

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.