Search Results (28)

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

Data-driven approaches and calibration techniques for mathematical models, starting from observed data, are attracting more and more interest in the field of civil engineering. Among them, evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that combines genetic algorithms (GAs) and regression strategies. However, the difficulties and uncertainties inherent in the method have pointed out how the implementation of proper computational methods together with the use of recent and qualified databases of experimental data are essential to carry out reliable formulations. In this framework, this paper explores a new robust EPR approach able to remove potential outliers and leverage points often occurring in biased dataset and simultaneously accounting for the effects of probabilistic uncertainties. Uncertainties are incorporated in the EPR methodology by adopting the direct perturbation method. In particular, it is shown the importance to set the parameters representative of experimental and analytical dispersions on the basis of the characteristics of the database in terms of homogeneity. With this purpose, two different case-studies are analyzed, dealing with the shear capacity of RC beams without stirrups and the compressive strength of cement-based mortar specimens, respectively. Finally, the best capacity equations are selected and discussed. Full article

The traditional design of foundations in soft clay often relies on large-diameter piles, which, although effective, are costly and impractical for low- to medium-rise buildings. Micropiles have emerged as a cost-effective alternative, offering an efficient solution to these challenges. To advance the adoption of micropiles in geotechnical practice, this study employs a multi-objective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA), a hybrid artificial intelligence method, to develop a robust and straightforward model for predicting the load–settlement response of micropiled rafts in cohesive soils under vertical loads. The model was created using an extensive database comprising 458 data points derived from field tests, centrifuge experiments, laboratory studies, and numerical simulations reported in the literature. This comprehensive database covers a wide range of scenarios by varying key parameters of micropiles within a group, including their length, diameter, number, spacing, construction method, and raft thickness. The proposed EPR model could deliver accurate predictions, providing a practical approach for geotechnical applications. In addition, the predictions of the model could support the conclusion that pressure-grouted micropiles are more efficient than gravity-grouted ones in enhancing the performance of micropiled rafts. Full article

This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRP-strengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams. Full article

In nature, closely related species often exhibit diverse characteristics, challenging simplistic line interpretations of trait evolution. For these species, the evolutionary dynamics of one trait may differ markedly from another, with some traits evolving at a slower pace and others rapidly diversifying. In light of this complexity and concerning the phenomenon of trait relationships that escape line measurement, we introduce a novel general adaptive optimal regression model, grounded on polynomial relationships. This approach seeks to capture intricate patterns in trait evolution by considering them as continuous stochastic variables along a phylogenetic tree. Using polynomial functions, the model offers a holistic and comprehensive description of the traits of the studied species, accounting for both decreasing and increasing trends over evolutionary time. We propose two sets of optimal adaptive evolutionary polynomial regression models of $k^{th}$ order, named the Ornstein–Uhlenbeck Brownian Motion Polynomial (${\mathrm{OUBMP}}_k$) model and Ornstein–Uhlenbeck Ornstein–Uhlenbeck Polynomial (${\mathrm{OUOUP}}_k$) model, respectively. Assume that the main trait value $y_t$ is a random variable of the Ornstein–Uhlenbeck (OU) process and that its optimal adaptive value ${\theta }_t^y$ has a polynomial relationship with other traits $x_t$ for statistical modeling, where $x_t$ can be a random variable of Brownian motion (BM) or OU process. As analytical representations for the likelihood of the models are not feasible, we implement an approximate Bayesian computation (ABC) technique to assess the performance through simulation. We also plan to apply models to the empirical study using the two datasets: the longevity vs. fecundity in the Mediterranean nekton group, and the trophic niche breadth vs. body mass in carnivores in a European forest region. Full article

One of the main problems of water distribution systems is the management and the evaluation of water losses. At the Laboratory of Hydraulics and Maritime Constructions at the Università Politecnica delle Marche, experimental research on this topic was conducted to measure the water volume exiting from a known shape and size hole at fixed hydraulic conditions. The obtained results were also used as input data for the Evolutionary Polynomial Regression (EPR) analysis for the construction of prediction models that could be employed for the management of water leakages in pressurized networks. Full article

This paper investigates the adequacy of radial basis function (RBF)-based models as surrogates in uncertainty quantification (UQ) and CFD shape optimization; for the latter, problems with and without uncertainties are considered. In UQ, these are used to support the Monte Carlo, as well as, the non-intrusive, Gauss Quadrature and regression-based polynomial chaos expansion methods. They are applied to the flow around an isolated airfoil and a wing to quantify uncertainties associated with the constants of the $\gamma -\tilde{R}{e}_{\theta t}$ transition model and the surface roughness (in the 3D case); it is demonstrated that the use of the RBF-based surrogates leads to an up to 50% reduction in computational cost, compared with the same UQ method that uses CFD computations. In shape optimization under uncertainties, solved by stochastic search methods, RBF-based surrogates are used to compute statistical moments of the objective function. In applications with geometric uncertainties which are modeled through the Karhunen–Loève technique, the use on an RBF-based surrogate reduces the turnaround time of an evolutionary algorithm by orders of magnitude. In this type of applications, RBF networks are also used to perform mesh displacement for the perturbed geometries. Full article

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models. Full article

Cement-grouted injections are increasingly employed as a countermeasure material against liquefaction in active seismic areas; however, there is no methodology to thoroughly and directly evaluate the liquefaction potential of saturated sand materials reinforced by the cement grout-injected micropiles. To this end, first, a series of 1 g shaking table model tests are conducted. Time histories of pore water pressures, excess pore water pressure ratios (r_u), and the number of required cycles (N_peak) to liquefy the soil are obtained and modified lower and upper boundaries are suggested for the potential of liquefaction of both pure and grout-reinforced sand. Next, adopting genetic programming and the least square method in the framework of the evolutionary polynomial regression technique, high-accuracy predictive equations are developed for the estimation of r_umax. Based on the results of a three-dimensional, graphical, multiple-variable parametric (MVP) analysis, and introducing the concept of the critical, boundary inclination angle, the inclination of micropiles is shown to be more effective in view of liquefaction resistivity for loose sands. Due to a lower critical boundary inclination angle, the applicability range for inclining micropiles is narrower for the medium-dense sands. MVP analyses show that the effects of a decreasing spacing ratio on decreasing r_umax are amplified while micropiles are inclined. Full article

The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8% and a coefficient of determination R² of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations. Full article

This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings with width (B) and rested on a bi-layered profile with top layer thickness (h) and bottom layer thickness (H). The soil was modeled using the well-known Mohr-Coulomb’s constitutive law. The extracted load-settlement curve from each FEM model is approximated to hyperbolic function and its factors (a, b) were determined. The subgrade reaction value (Ks) is the (stress/settlement), hence (1/Ks = a·Δ + b). Both inputs and outputs of the parametric study were collected in a single database containing the geometrical factors (B, h & H), soil properties of the top and bottom layers (c, φ & γ) and the extracted hyperbolic factors (a, b). Finally, three AI techniques—Genetic Programming (GP), Evolutionary Polynomial Regression (EPR) and Artificial Neural Networks (ANN)—were implemented to develop three predictive models to estimate the values of (a, b) using the collected database. The three developed models showed different accuracy values of (50%, 65% and 80%) for (GP, EPR and ANN), respectively. The innovation of the developed model is its ability to capture the degradation of a subgrade reaction by increasing the stress (or the settlement) according to the hyperbolic formula. Full article

The strength of concrete elements under shear is a complex phenomenon, which is induced by several effective variables and governing mechanisms. Thus, each parameter’s importance depends on the values of the effective parameters and the governing mechanism. In addition, the new concrete types, including lightweight concrete and fibered concrete, add to the complexity, which is why machine learning (ML) techniques are ideal to simulate this behavior due to their ability to handle fuzzy, inaccurate, and even incomplete data. Thus, this study aims to predict the shear strength of both normal-weight and light-weight concrete beams using three well-known machine learning approaches, namely evolutionary polynomial regression (EPR), artificial neural network (ANN) and genetic programming (GP). The methodology started with collecting a dataset of about 1700 shear test results and dividing it into training and testing subsets. Then, the three considered (ML) approaches were trained using the training subset to develop three predictive models. The prediction accuracy of each developed model was evaluated using the testing subset. Finally, the accuracies of the developed models were compared with the current international design codes (ACI, EC2 & JSCE) to evaluate the success of this research in terms of enhancing the prediction accuracy. The results showed that the prediction accuracies of the developed models were 68%, 83% & 76.5% for GP, ANN & EPR, respectively, and 56%, 40% & 62% for ACI, EC2 & JSCE, in that order. Hence, the results indicated that the accuracy of the worst (ML) model is better than those of design codes, and the ANN model is the most accurate one. Full article

The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse settlement and coefficient of stress release of sandy gravel soil through evolutionary polynomial regression (EPR). To achieve this, a dataset containing 180 records obtained from a large-scale direct shear test was used. In this study, five models were developed with the secant hyperbolic, tangent hyperbolic, natural logarithm, exponential, and sinusoidal inner functions. Using sand content (SC), normal stress (${\sigma }_n$), shear stress level (SL), and relative density (Dr) values, the models can predict the collapse settlement (∆H) and coefficient of stress release (CSR). The results indicated that the models developed with the exponential functions were the best models. With these models, the values of $R^2$ for training, testing, and all data in the prediction of collapse settlement were 0.9759, 0.9759, and 0.9757, respectively, and the values of $R^2$ in predicting the coefficient of stress release were 0.9833, 0.9820, and 0.9833, respectively. The sensitivity analysis also revealed that the sand content (SC) and relative density (Dr) parameters had the highest and lowest degrees of importance in predicting collapse settlement. In contrast, the Dr and SC parameters showed the highest and lowest degrees of importance in predicting the coefficient of stress release. Finally, the conducted parametric study showed that the developed models were in line with the results of previous studies. Full article

Steel plates are used in the construction of various structures in civil engineering, aerospace, and shipbuilding. One of the main failure modes of plate members is buckling. Openings are provided in plates to accommodate various additional facilities and make the structure more serviceable. The present study examined the critical buckling load of rectangular steel plates with centrally placed circular openings and different support conditions. Various datasets were compiled from the literature and integrated into artificial intelligence techniques like Gene Expression Programming (GEP), Artificial Neural Network (ANN) and Evolutionary Polynomial Regression (EPR) to predict the critical buckling loads of the steel plates. The comparison of the developed models was conducted by determining various statistical parameters. The assessment revealed that the ANN model, with an R² of 98.6% with an average error of 10.4%, outperformed the other two models showing its superiority in terms of better precision and less error. Thus, artificial intelligence techniques can be adopted as a successful technique for the prediction of the buckling load, and it is a sustainable method that can be used to solve practical problems encountered in the field of civil engineering, especially in steel structures. Full article

The complicated flow conditions and massive design parameters bring two main difficulties to the aerodynamic optimization of axial compressors: expensive evaluations and numerous optimization variables. To address these challenges, this paper establishes a novel fast aerodynamic optimization platform for axial compressors, consisting of a radial basic function (RBF)-based blade parameterization method, a data-driven differential evolution optimizer, and a computational fluid dynamic (CFD) solver. As a versatile interpolation method, RBF is used as the shape parameterization and deformation technique to reduce optimization variables. Aiming to acquire competitive solutions in limited steps, a data-driven evolution optimizer is developed, named the pre-screen surrogate model assistant differential evolution (pre-SADE) optimizer. Different from most surrogate model-assisted evolutionary algorithms, surrogate models in pre-SADE are used to screen the samples, rather than directly estimate them, in each generation to reduce expensive evaluations. The polynomial regression model, Kriging model, and RBF model are integrated in the surrogate model to improve the accuracy. To further save optimization time, the optimizer also integrates parallel task management programs. The aerodynamic optimization of a transonic rotor (NASA Rotor 37) is performed as the validation of the platform. A differential evolution (DE) optimizer and another surrogate model-assisted algorithm, committee-based active learning for surrogate model assisted particle swarm optimization (CAL-SAPSO), are introduced for the comparison runs. After optimization, the adiabatic efficiency, total pressure ratio, and surge margin are, respectively, increased by 1.47%, 1.0%, and 0.79% compared to the initial rotor. In the same limited steps, pre-SADE gets a 0.57% and 0.51% higher rotor adiabatic efficiency than DE and CAL-SAPSO, respectively. With the help of parallel techniques, pre-SADE and DE save half the optimization time compared to CAL-SAPSO. The results verify the effectiveness and the rapidity of the fast aerodynamic optimization platform. Full article