Evaluating the suitability of incorporating sugarcane bagasse ash, polypropylene fibers, and sea sand-seawater in enhancing physico-mechanical properties of lightweight foamed concrete.

This study aims to explore the feasibility of replacing traditional components, such as Portland cement, river sand and tap water with sugarcane bagasse ash (SCBA), polypropylene (PP) fibers, and sea sand-seawater (SSSW) in lightweight foamed concrete (LWFC) production. SCBA was used in the range from 0 to 15% as cement replacement, and PP fibers were used with dosage from 0% to 1% by volume of LWFC. Meanwhile, SSSW was used to completely replace river sand and tap water.

Predicting and evaluating settlement of shallow foundation using machine learning approach.

This study presents a novel approach to accurately predict the settlement of shallow foundations using advanced machine learning techniques while assessing the influence of key variables. Four machine learning models Gradient Boosting (GB), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are enhanced with Particle Swarm Optimization (PSO) for hyperparameter tuning, resulting in hybrid models GB-PSO, RF-PSO, SVM-PSO, and KNN-PSO. The experimental dataset comprises 189 samples, and model performance is rigorously evaluated through K-Fold Cross-Validation alongside R², RMSE, MAE, and MAPE metrics.

The research explores the predictive capacity of the shear strength of reinforced concrete walls with different cross-sectional shapes using the XGBoost model.

Structurally, the lateral load-bearing capacity mainly depends on reinforced concrete (RC) walls. Determination of flexural strength and shear strength is mandatory when designing reinforced concrete walls. Typically, these strengths are determined through theoretical formulas and verified experimentally.

Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods.

Higher education is regarded as being of paramount importance in Vietnam and as being essential to raising the level of the country's labor force and promoting economic progress. Evaluation of lecturers is one of the institution's activities and a crucial component of managing human resources in higher education institutions. How to evaluate faculty members' overall performance using a range of criteria is one of the key evaluation-related challenges.

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques.

An on-site and portable electrochemical sensing platform based on spinel zinc ferrite nanoparticles for the quality control of paracetamol in pharmaceutical samples.

In this study, crystalline spinel zinc ferrite nanoparticles (ZnFeO NPs) were successfully prepared and proposed as a high-performance electrode material for the construction of an electrochemical sensing platform for the detection of paracetamol (PCM). By modifying a screen-printed carbon electrode (SPE) with ZnFeO NPs, the electrochemical characteristics of the ZnFeO/SPE and the electrochemical oxidation of PCM were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), chronoamperometry (CA), and differential pulse voltammetry (DPV) methods. The calculated electrochemical kinetic parameters from these techniques including electrochemically active surface area (ECSA), peak-to-peak separation (Δ), charge transfer resistance (), standard heterogeneous electron-transfer rate constants (), electron transfer coefficient (), catalytic rate constant (), adsorption capacity (), and diffusion coefficient () proved that the as-synthesized ZnFeO NPs have rapid electron/mass transfer characteristics, intrinsic electrocatalytic activity, and facilitate the adsorption-diffusion of PCM molecules towards the modified electrode surface.

Machine learning approaches for predicting Cracking Tolerance Index (CTIndex) of asphalt concrete containing reclaimed asphalt pavement.

One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete's crack resistance is represented by the CT index.

Incorporating Industrial By-Products into Geopolymer Mortar: Effects on Strength and Durability.

In recent years, the reuse of industrial waste has become increasingly important for sustainable development. Therefore, this study investigated the application of granulated blast furnace slag (GBFS) as a cementitious replacement material in fly-ash-based geopolymer mortar containing silica fume (GMS). The performance changes in the GMS samples manufactured with different GBFS ratios (0-50 wt%) and alkaline activators were evaluated.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research.

An insight of light-enhanced electrochemical kinetic behaviors and interfacial charge transfer of CuInS/MoS-based sensing nanoplatform for ultra-sensitive detection of chloramphenicol.

Owing to the effective combination between MoS sheets with CuInS nanoparticles (NPs), a direct Z-scheme heterojunction was successfully constructed and proved as a promising structure to modify the working electrode surface with the aim of enhancing overall sensing performance towards CAP detection. Herein, MoS was employed as a high mobility carrier transport channel with a strong photo-response, large specific surface area, and high in-plane electron mobility, while CuInS acted as an efficient light absorber. This not only offered a stable nanocomposite structure but also created impressive synergistic effects of high electron conductivity, large surface area, highlight exposure interface, as well as favorable electron transfer process.

Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer.

This paper seeks to develop an interpretable Machine Learning (ML) model for predicting the unconfined compressive strength (UCS) of cohesive soils stabilized with geopolymer at 28 days. Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. The database consists of 282 samples collected from the literature with three different types of cohesive soil stabilized with three geopolymer categories including Slag-based geopolymer cement, alkali-activated fly ash geopolymer and slag/fly ash-based geopolymer cement.

A molybdenum disulfide/nickel ferrite-modified voltammetric sensing platform for ultra-sensitive determination of clenbuterol under the presence of an external magnetic field.

The electrochemical behavior and sensing performance of an electrode modified with NiFeO (NFO), MoS, and MoS-NFO were thoroughly investigated using CV, EIS, DPV, and CA measurements, respectively. MoS-NFO/SPE provided a higher sensing performance towards the detection of clenbuterol (CLB) than other proposed electrodes. After optimization of pH and accumulation time, the current response recorded at MoS-NFO/SPE linearly increased with an increase of CLB concentration in the range from 1 to 50 μM, corresponding to a LOD of 0.

A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods.

Over time, machine learning methods have developed, but there have not been many studies comparing how well they predict ignition delays. In this study, a model that forecasts the ignition delay of a diesel engine utilizing diesel fuel and biodiesel fuel was developed using Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques. This work has clarified the problems in designing and training the model.

Advances in magnetic field-assisted electrolyte's physicochemical properties and electrokinetic parameters: A case study on the response ability of chloramphenicol on FeO@carbon spheres-based electrochemical nanosensor.

Despite the utilization of external magnetic field (MF) in promoting the intrinsic unique features of magnetic nanomaterials in many different applications has been reported, however the origin of MF-dependent electrochemical behaviors as well as the electrochemical response of analytes at the electrode in sensor applications is still not clear. In this report, the influence of MF on the electrolyte's physicochemical properties (polarization, mass transport, charge/electron transfer) and electrode's properties (conductivity, morphology, surface area, interaction, adsorption capability, electrocatalytic ability) was thoroughly investigated. Herein, the working electrode surface was modified with carbon spheres (CSs), magnetic nanoparticles (FeONPs), and their nanocomposites (FeO@CSs), respectively.

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process.

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria.

Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR).

Developing random forest hybridization models for estimating the axial bearing capacity of pile.

Accurate determination of the axial load capacity of the pile is of utmost importance when designing the pile foundation. However, the methods of determining the axial load capacity of the pile in the field are often costly and time-consuming. Therefore, the purpose of this study is to develop a hybrid machine-learning to predict the axial load capacity of the pile.

Predicting sustainable arsenic mitigation using machine learning techniques.

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.

Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression.

This study aims to investigate the influence of all the mixture components of high-performance concrete (HPC) on its early compressive strength, ranging from 1 to 14 days. To this purpose, a Gaussian Process Regression (GPR) algorithm was first constructed using a database gathered from the available literature. The database included the contents of cement, blast furnace slag (BFS), fly ash (FA), water, superplasticizer, coarse, fine aggregates, and testing age as input variables to predict the output of the problem, which was the early compressive strength.

Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS.

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database.

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils.

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET).

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering.

Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time-consuming and costly.

Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam.

Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model.

Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams.

In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement.