The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash-Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites

A recently introduced bendable concrete having hundred times greater strain capacity provides promising results in repair of engineering structures, known as strain hardening cementitious composites (SHHCs). The current research creates new empirical prediction models to assess the mechanical properties of strain-hardening cementitious composites (SHCCs) i.e., compressive strength (CS), first crack tensile stress (TS), and first crack flexural stress (FS), using gene expression programming (GEP). Wide-ranging records were considered with twelve variables i.e., cement percentage by weight (C%), fine aggregate percentage by weight (Fagg%), fly-ash percentage by weight (FA%), Water-to-binder ratio (W/B), super-plasticizer percentage by weight (SP%), fiber amount percentage by weight (Fib%), length to diameter ratio (L/D), fiber tensile strength (FTS), fiber elastic modulus (FEM), environment temperature (ET), and curing time (CT). The performance of the models was deduced using correlation coefficient (R) and slope of regression line. The established models were also assessed using relative root mean square error (RRMSE), Mean absolute error (MAE), Root squared error (RSE), root mean square error (RMSE), objective function (OBF), performance index (PI) and Nash-Sutcliffe efficiency (NSE). The resulting mathematical GP-based equations are easy to understand and are consistent disclosing the originality of GEP model with R in the testing phase equals to 0.8623, 0.9269, and 0.8645 for CS, TS and FS respectively. The PI and OBF are both less than 0.2 and are in line with the literature, showing that the models are free from overfitting. Consequently, all proposed models have high generalization with less error measures. The sensitivity analysis showed that C%, Fagg%, and ET are the most significant variables for all three models developed with sensitiveness index higher than 10 %. The result of the research can assist researchers, practitioners, and designers to assess SHCC and will lead to sustainable, faster, and safer construction from environment-friendly waste management point of view.

Prediction and forewarning of axial force of steel bracing in foundation pit based on verhulst model

The axial force of steel bracing is one of the essential indexes to measure the stability of the bracing system of a foundation pit. The steel bracing system of a foundation pit in Ningbo City, China was taken as the research object to guarantee the stability of the steel bracing system of the foundation pit. Besides, the change of axial force between the two steel bracing structures was analyzed to predict the axial force data of the steel bracing and perform the safety forewarning of the steel bracing system. Firstly, GM (1,1) and Verhulst models in the gray model were selected for prediction based on the characteristics of poor information and the small sample size of original monitoring data of the steel bracing. Secondly, the precision of the GM (1,1) model and Verhulst model was compared to determine a more accurate prediction method. Finally, the safety forewarning model of the confidence interval estimation method was established based on the data obtained from the prediction model and the deformation characteristics and indexes of the steel bracing. With the significance levels α = 5% and α = 2% as the demarcating points, the forewarning grades of the steel bracing system of the deep foundation pit were divided, and then the operating state of the current steel bracing system was determined. The results demonstrated that the Verhulst model had better prediction precision compared with the ordinary GM (1, 1) model. Besides, the steel bracing system was in the safe operation range, and the judgment results of the model were consistent with the actual situation of the foundation pit of the steel bracing system. Thus, the Verhulst prediction model and the confidence interval security early forewarning model could be used to judge the stability of the steel bracing system.

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (P_sUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). An extensive database comprising 168 P_s and 145 UCS records was established after a comprehensive literature search. The nine most influential and easily determined geotechnical parameters were taken as the predictor variables. The network was trained and tested, and the predictions of the proposed models were compared with the observed results. The performance of all the models was tested using mean absolute error (MAE), root squared error (RSE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), regression coefficient (R²) and relative root mean square error (RRMSE). The sensitivity analysis indicated that the increasing order of inputs importance in case of P_s followed the order: maximum dry density MDD (30.5%) > optimum moisture content OMC (28.7%) > swell percent SP (28.1%) > clay fraction CF (9.4%) > plasticity index PI (3.2%) > specific gravity G_s (0.1%), whereas, in case of UCS it followed the order: sand (44%) > PI (26.3%) > MDD (16.8%) > silt (6.8%) > CF (3%) > SP (2.9%) > G_s (0.2%) > OMC (0.03%). Parametric analysis was also performed and the resulting trends were found to be in line with findings of past literature. The comparison results reflected that GEP and ANN are efficacious and reliable techniques for estimation of P_sUCS-ES. The derived mathematical GP-based equations portray the novelty of GEP model and are comparatively simple and reliable. The R_overall values for P_sUCS-ES followed the order: ANN > GEP > ANFIS, with all values lying above the acceptable range of 0.80. Hence, all the proposed AI approaches exhibit superior performance, possess high generalization and prediction capability, and evaluate the relative importance of the input parameters in predicting the P_sUCS-ES. The GEP model outperformed the other two models in terms of closeness of training, validation and testing data set with the ideal fit (1:1) slope. Evidently the findings of this study can help researchers, designers and practitioners to readily evaluate the swell-strength characteristics of the widespread expansive soils thus curtailing their environmental vulnerabilities which leads to faster, safer and sustainable construction from the standpoint of environment friendly waste management.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.