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 (PUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e.

Salicylic acid-mitigates abiotic stress tolerance altering defense mechanisms in (L.).

Under the changing climate due to global warming, various abiotic stresses including drought (D) and salinity (S) are expected to further trigger their devastating effects on the already vulnerable crop production systems. This experiment was designed to unravel and quantify the potential role of exogenous application of salicylic acid (SA) in mitigating both D and S stresses and their combination (D+S), with three replications using CRD (Completely Randomized Design). The obtained results of the current study demonstrated significant effects of all three types of stresses (D, S, and D+S) on various parameters in plants.

Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming.

The safety and economy of an infrastructure project depends on the material and design equations used to simulate the performance of a particular member. A variety of materials can be used in conjunction to achieve a composite action, such as a hollow steel section filled with concrete, which can be successfully utilized in the form of an axially loaded member. This study aims to model the ultimate compressive strength (P) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP).

Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming.

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach.

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches.

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet−dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here.

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach.

Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e.

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models.

An accurate calculation of the flexural capacity of flexural members is vital for the safe and economical design of FRP reinforced structures. The existing empirical models are not accurately calculating the flexural capacity of beams and columns. This study investigated the estimation of the flexural capacity of beams using non-linear capabilities of two Artificial Intelligence (AI) models, namely Artificial neural network (ANN) and Random Forest (RF) Regression.

Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils.

Rapid industrialization is leading to the pollution of underground natural soil by alkali concentration which may cause problems for the existing expansive soil in the form of producing expanding lattices. This research investigates the effect of stabilizing alkali-contaminated soil by using fly ash. The influence of alkali concentration (2 N and 4 N) and curing period (up to 28 days) on the unconfined compressive strength (UCS) of fly ash (FA)-treated (10%, 15%, and 20%) alkali-contaminated kaolin and black cotton (BC) soils was investigated.

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models.

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures.

Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming.

The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)—i.e., fly ash (FA) and silica fume (SF)—on the 28-day compressive strength (CS28d) of cementitious grouts by using.

Mechanical and microstructural performance of concrete containing high-volume of bagasse ash and silica fume.

In this study, researchers examined the effect of replacing a high-volume of cement with sugarcane bagasse ash (BA) and silica fume (SF). In addition to the control, three binary and three ternary blends of concrete containing different percentages of cement/BA and cement/BA/SF were tested to determine the various mechanical and microstructural properties of concrete. For each mix, eighteen cylindrical concrete specimens were cast followed by standard curing (moist at 20 °C) to test the compressive and tensile strengths of three identical specimens at 7, 28, and 91 days.

Experimental Investigation of the Stress-Strain Behavior and Strength Characterization of Rubberized Reinforced Concrete.

Due to the rapid increase in population, the use of automobile vehicles increases day by day, which causes a considerable increase in the waste tires produced worldwide. Research studies are in progress to utilize scrap tires and waste rubber material in several fields to cater the pollution problems in a sustainable and environmentally friendly manner. In this research, the shredded waste tires were used in concrete to replace fine aggregates in different percentages.

An Investigation on the Behaviour of Geosynthetic Reinforced Quarry Waste Bases (QWB) Under Vertical loading.

The strength and rigidity of base course can significantly affect the performance of pavements. The rigidity of roadways relies on the infill material used in base layers which is interdependent on its thickness and quality. With the increase in the base thickness, the performance of the base course improves but the cost associated with it also increases.

Cracking propagation in expansive soils under desiccation and stabilization planning using Bayesian inference and Markov decision chains.

Desiccation cracking endangers the stability of expansive soils subjected to cyclic moisture variations. In the current research, prominent cracking prediction models including linear, linear elastic, linear elastoplastic, and linear elastic fracture were studied. Then, Monte Carlo limit state functions were generated based on predictions.

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).

Fractal approach in expansive clay-based materials with special focus on compacted GMZ bentonite in nuclear waste disposal: a systematic review.

Knowledge of the behavior of highly compacted expansive clays, as an engineered barrier, in disposal of high-level nuclear waste (HLW) systems to prevent the pollution due to migration of radionuclide is extremely essential. The prominent properties of globally and widely used bentonites have been extensively studied during past two decades. In China, GaoMiaoZi (GMZ) bentonite is the first choice as a buffer or backfill material for deep geological repositories.

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 (PUCS-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 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.