Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models

The long-term durability of glass fiber reinforced polymer(GFRP)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural codes.The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars.Therefore,three robust metaheuristic algorithms,namely particle swarm optimiza-tion(PSO),genetic algorithm(GA),and support vector machine(SVM),were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system(ANFIS)in order to obtain more accurate prediction model.Various optimized models were developed to predict the tensile strength retention(TSR)of degraded GFRP rebars in typical alkaline environments(e.g.,seawater sea sand concrete(SWSSC)environment in this study).The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging.A to-tal number of 715 experimental laboratory samples were collected in a form of extensive database to be trained.K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal folds.In order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were performed.It was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned GFRP bars.In the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete en-vironment.The sensitivity analysis revealed GFRP bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR.

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete.The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm(CFA)to evolve a rational and efficient predictive model.The CFA optimizer is augmented with chaotic maps and Levy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete(RAC).A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm.A dataset composite of nine effective parameters,including concrete components and fundamental characteristics of recycled aggregate(RA),is used as input to predict the migration coefficient of strengthened RAC as output,k-fold cross validation algorithm is utilized to validate the hybrid algorithm.Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability.Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction.In addition,the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.