Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models.

This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested.

Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network.

Heavy metal adsorption onto biochar is an effective method for the treatment of the heavy metal contamination of water and wastewater. This study aims to evaluate the heavy metals sorption efficiency of different biochar characteristics and propose a novel intelligence method for predicting the sorption efficiency of heavy metal onto biochar with high accuracy based on the back-propagation neural network (BPNN) and fuzzy C-means clustering algorithm (FCM), named as FCM-BPNN. Accordingly, the FCM algorithm was used to simulate the properties of metal adsorption data and divide them into clusters with similar features.

Dynamic Stability Analysis in Hybrid Nanocomposite Polymer Beams Reinforced by Carbon Fibers and Carbon Nanotubes.

The objective of this innovative research is assessment of dynamic stability for a hybrid nanocomposite polymer beam. The considered beam formed by multiphase nanocomposite, including polymer-carbon nanotubes (CNTs)-carbon fibers (CFs). Hence, as to compute the effective material characteristics related to multiphase nanocomposite layers, the Halpin-Tsai model, as well as micromechanics equations are employed.

The role of inter-particle friction on rheology and texture of wet granular flows.

In order to get insight into the rheology and texture of rough unsaturated granular flows, we study the effects of the inter-particle friction coefficient on the macroscopic attributes and the texture variables of steady-state shearing flow of wet granular materials by relying on three-dimensional (3D) particle dynamics simulations. The macroscopic attributes are characterized by the macroscopic friction coefficient, macroscopic cohesion, and the packing fraction. The microstructural variables are characterized by the fabric and force anisotropies, the coordination number, and the stress transmission ratio.

Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm.

In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines. For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules-GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters. The factor of safety was used as the model outcome.

Reliability Analysis of FRP-Confined Concrete at Ultimate using Conjugate Search Direction Method.

In this paper compressive strength and ultimate strain results in the current database of fiber-reinforced polymer (FRP)-confined concrete are used to determine the reliability of their design space. The Lognormal, Normal, Frechet, Gumbel, and Weibull distributions are selected to evaluate the probabilistic characteristics of six FRP material categories. Following this, safety levels of the database are determined based on a probabilistic model.

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms.

In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model.

SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.

In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (E). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process.

Magnetohydrodynamic nanofluid radiative thermal behavior by means of Darcy law inside a porous media.

Radiative nanomaterial thermal behavior within a permeable closed zone with elliptic hot source is simulated. Darcy law is selected for simulating permeable media in existence of magnetic forces. Contour plots for various buoyancy, Hartmann numbers and radiation parameter were illustrated.