Exploration on Robustness of Exponentially Global Stability of Recurrent Neural Networks with Neutral Terms and Generalized Piecewise Constant Arguments

With a view to the interference of piecewise constant arguments (PCAs) and neutral terms (NTs) to the original system and the significant applications in the signal transmission process, we explore the robustness of the exponentially global stability (EGS) of recurrent neural network (RNN) with PCAs and NTs (NPRNN). The following challenges arise: what the range of PCAs and the scope of NTs can NPRNN tolerate to be exponentially stable. So we derive two important indicators: maximum interval length of PCAs and the scope of neutral term (NT) compression coefficient here for NPRNN to be exponentially stable. Additionally, we theoretically proved that if the interval length of PCAs and the bound of NT compression coefficient are all lower than the given results herein, the disturbed NPRNN will still remain global exponential stability. Finally, there are two numerical examples to verify the deduced results’ effectiveness here.

Accuracy assessment of extreme learning machine in predicting soil compression coefficient

The compression coefficient (Cc) is an important soil mechanical parameter that represents soil compressibility in the process of consolidation. In this study, a machine learning derived model, namely extreme learning algorithm (ELM), was used to predict the Cc of soil. A total of 189 experimental results were used and randomly divided to construct the training and testing parts for the development and validation of ELM. Monte Carlo approach was applied to take into account the random sampling of samples constituting the training dataset. A number of 13 input parameters reflecting the experiment were used as the input variables to predict the output Cc. Several statistical criteria, such as mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R) and the Monte Carlo convergence estimator were used to assess the performance of ELM in predicting the Cc of soil. The results showed that ELM had a strong capacity to predict the Cc of soil, with the R value > 0.95. The convergence of results, as well as the capability of ELM were fully investigated to understand the advantage of using ELM as a predictor.

A Novel Application of League Championship Optimization (LCA): Hybridizing Fuzzy Logic for Soil Compression Coefficient Analysis

Employing league championship optimization (LCA) technique for adjusting the membership function parameters of the adaptive neuro-fuzzy inference system (ANFIS) is the focal objective of the present study. The mentioned optimization is carried out for better estimation of the soil compression coefficient (SCC) using twelve key factors of soil, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index. This information is widely useable in designing high-rise buildings located in smart cities. Notably, the used data is collocated from a real-world construction project in Vietnam. The hybrid ensemble of LCA-ANFIS is developed, and the best structure is determined by a three-step sensitivity analysis process. The prediction accuracy of the proposed hybrid model is compared with typical ANFIS to examine the efficiency of the combined LCA. Based on the results, applying the LCA algorithm lead to a 4.88% and 6.19% decrease in prediction error, in terms of root mean square error and mean absolute error, respectively. Moreover, the correlation index rose from 0.7351 to 0.7539, which indicates the higher consistency of the hybrid model results. Due to the acceptable accuracy of the proposed LCA-ANFIS model, it can be a promising alternative to common empirical and laboratory methods.

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R2) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.