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Abstract This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (P s UCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the P s UCS-ES. The models were trained using the nine most influential parameters affecting P s UCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R 2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for P s-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted P s UCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Extensive research has explored cracking in asphalt mixtures under pure mode-I conditions; however, heavy traffic loads often lead to crack formation under mixed-mode fracture conditions in asphalt pavements. This paper presents a phase field damage model to simulate mixed-mode I-II fracture phenomena observed in asphalt concrete. In contrast to the conventional approach employing cohesive zone models, which restrict crack propagation exclusively to the peripheries of interface elements within the computational mesh, the model presented herein can simulate crack evolution throughout the material domain. Crack inception and material degradation were modeled using a unified failure criterion and a modified G-criterion. Mode I and mixed-mode I-II asphalt fracture experiments reported in the literature are replicated using the phase field model. The results show that the model can effectively simulate the fracture response with sufficient accuracy. The effect of the offset ratio on the peak load, dissipation energy, mode-mixity, and mode of failure is studied. In addition, the fracture response under different failure criteria is simulated using the parameter χ. It is concluded that with an increase in notch offset, the peak load, dissipated energy, and mode-mixity increase. Furthermore, it is demonstrated that in the case of the provided notched beam with a mere 23 % mode II contribution, the modified and tensile failure criterion-based PFMs are equivalent for χ ≥ 0.707.

The optimized use of nanomaterials in concrete for developing resilient and eco-friendly infrastructure is vital for our built environment. Therefore, the current study aims to optimize the dosages and particle sizes of colloidal nano-silica in developing high-performance, sustainable cement mortar. For this purpose, compressive strength, water absorption, porosity, and microstructure tests were performed on mortars containing different dosages and particle sizes (NS80, NS250, NS250Al, NS500) of colloidal nano-silica. For comparison, tests were also performed on control mortars and mortars containing metakaolin and silica fume. The microstructure of cement pastes was examined through SEM-based BSE imaging with elemental mapping through EDS and FTIR analyses. Additionally, global warming potential (GWP) as well as embodied energy (EE) were also analyzed for all mortar mixtures. The results demonstrated that smaller nano-sized particles (NS500) at a lower dosage (3%) showed improved compressive strength and densified microstructure compared to all other mixes. However, larger nano-sized particles (NS80) at medium (5%) and high dosages (10%) had the highest impact on compressive strengths due to the formation of low and medium Ca-based C-S-H and C-A-S-H phases. Furthermore, the results of greater compressive strength of NS80 mortars under medium and high dosages were supported by their lowest water absorption and apparent porosity. When compared with the control and all other mixes, the mortar mixes containing medium and high doses of NS80 had lower embodied energy/MPa and CO2-eq intensity/MPa, indicating their significant contribution to producing green concrete with a lower environmental impact.

The long-term durability of glass fiber reinforced polymer (GFRP) bars in harsh alkaline environments is of great importance in engineering, which is reflected by the environmental reduction factor in various structural codes. The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars. Therefore, three robust metaheuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM), were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system (ANFIS) in order to obtain more accurate prediction model. Various optimized models were developed to predict the tensile strength retention (TSR) of degraded GFRP rebars in typical alkaline environments (e.g., seawater sea sand concrete (SWSSC) environment in this study). The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging. A total number of 715 experimental laboratory samples were collected in a form of extensive database to be trained. K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal folds. In order to analyze the efficiency of the metaheuristic algorithms, multiple statistical tests were performed. It was concluded that the ANFIS-SVM-based model is robust and accurate in predicting the TSR of conditioned GFRP bars. In the meantime, the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete environment. The sensitivity analysis revealed GFRP bar size, volume fraction of fibers, and pH of solution were the most influential parameters of TSR.

Abstract Bridges are among the most vulnerable structures to earthquake damage. Most bridges are seismically inadequate due to outdated bridge design codes and poor construction methods in developing countries. Although expensive, experimental studies are useful in evaluating bridge piers. As an alternative, numerical tools are used to evaluate bridge piers, and many numerical techniques can be applied in this context. This study employs Abaqus/Explicit, a finite element program, to model bridge piers nonlinearly and validate the proposed computational method using experimental data. In the finite element program, a single bridge pier having a circular geometry that is being subjected to a monotonic lateral load is simulated. In order to depict damages, Concrete Damage Plasticity (CDP), a damage model based on plasticity, is adopted. Concrete crushing and tensile cracking are the primary failure mechanisms as per CDP. The CDP parameters are determined by employing modified Kent and Park model for concrete compressive behavior and an exponential relation for tension stiffening. The performance of the bridge pier is investigated using an existing evaluation criterion. The influence of the stress–strain relation, the compressive strength of concrete, and geometric configuration are taken into consideration during the parametric analysis. It has been observed that the stress–strain relation, concrete strength, and configuration all have a significant impact on the column response.

Abstract The current research presents a novel and sustainable load-bearing system utilizing cellular lightweight concrete block masonry walls. These blocks, known for their eco-friendly properties and increasing popularity in the construction industry, have been studied extensively for their physical and mechanical characteristics. However, this study aims to expand upon previous research by examining the seismic performance of these walls in a seismically active region, where cellular lightweight concrete block usage is emerging. The study includes the construction and testing of multiple masonry prisms, wallets, and full-scale walls using a quasi-static reverse cyclic loading protocol. The behavior of the walls is analyzed and compared in terms of various parameters such as force–deformation curve, energy dissipation, stiffness degradation, deformation ductility factor, response modification factor, and seismic performance levels, as well as rocking, in-plane sliding, and out-of-plane movement. The results indicate that the use of confining elements significantly improves the lateral load capacity, elastic stiffness, and displacement ductility factor of the confined masonry wall in comparison to an unreinforced masonry wall by 102%, 66.67%, and 5.3%, respectively. Overall, the study concludes that the inclusion of confining elements enhances the seismic performance of the confined masonry wall under lateral loading.

Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete.

AbstractIn an age of depleting earth due to global warming impacting badly on the ozone layer of the earth system, the need to employ technologies to substitute those engineering practices which result in emissions contributing to the death of our earth has arisen. One of those technologies is one that can sufficiently replace overdependence on laboratory activities where oxides of carbon and other toxins are released. Also, it is one technology that brings precision to other engineering activities especially earthwork design and construction thereby reducing to lower ebb the release of carbon oxides due to inexact utilization of materials during geotechnical practices. In this review, the use of artificial intelligence techniques in geotechnics has been explored as a precise technique through which geotechnical engineering works don’t impact on our planet due to precision. The intelligent learning algorithms of ANN, Fuzzy Logic, GEP, ANFIS, ANOVA and other nature-inspired algorithms have been reviewed as they are applied in the prediction of geotechnical and geoenvironmental problems and system. It is a complex exercise to conduct experimental protocols during the design and construction of earthwork infrastructures. Most times, such experimental exercises don’t meet the required condition for sustainable design and construction. At other times, certain errors as a result of experimental set up or human misjudgment may mar the accuracy of measurements and release unexpected emissions. The employment of the evolutionary learning methods has solved most of the lapses encountered in repeated laboratory measurements. So, in this review work, the relevant computational intelligent techniques employed at different times, under different laboratory protocols and utilizing different materials, have been presented as a comprehensive guide to future researchers in this innovative and evolving field of artificial intelligence. With this extensive review, a researcher would not have to look far to get a technical and state of the art guide in the utilization of various intelligent techniques that would enable engineering models in a more efficient, precise and more sustainable approach to forestall multiple practices that release carbon emissions into the environment.

Abstract This article presents that acrylate copolymers are the potential candidate against the adsorption of bovine serum albumin (BSA). A series of copolymers poly(methyl methacrylate) (pMMA), poly(3-sulfopropyl methacrylate-co-methyl methacrylate) p(SPMA-co-MMA), and poly(dimethylaminoethyl methacrylate-co-methyl methacrylate) p(DMAEMA-co-MMA) were synthesized via free radical polymerization. These amphiphilic copolymers are thermally stable with a glass transition temperature (Tg) 50–120 °C and observed the impact of surface charge on amphiphilic copolymers to control interactions with the bovine serum albumin (BSA). These copolymers pMD1 and pMS1 have surface charges, − 56.6 and − 72.6 mV at pH 7.4 in PBS buffer solution that controls the adsorption capacity of bovine serum albumin (BSA) on polymers surface. Atomic force microscopy (AFM) analysis showed minimum roughness of 0.324 nm and 0.474 nm for pMS1 and pMD1. Kinetic studies for BSA adsorption on these amphiphilic copolymers showed the best fitting of the pseudo-first-order model that showed physisorption and attained at 25 °C and pH 7.4 within 24 h.

Unlike the cohesive interface element model, the phase field model (PFM) is a newly developed computational model which provides a unified approach to predicting crack nucleation, propagation, coalescence and branching without any ad-hoc criterion. Over the years, several variants of the phase field model are presented. This paper aims to investigate the suitability of different phase field models and determine the optimal characteristic functions/model parameters for low-temperature fracture in asphalt mixtures. The analysis results of the phase field method (which belongs to a class of diffuse damage models) are also compared with the interface element model (which belongs to a class of discrete damage models). The models are compared by examining the governing equations and performing numerical simulations on single-edge notch specimens. Furthermore, various aspects of cohesive zone model (CZM) based PFM are discussed including the type of softening laws and damage growth under uniform/non-uniform stress states. It is concluded that brittle phase field models are not fully capable to predict fracture in asphalt mixtures even at low temperatures.

Expansive soils are problematic for direct engineering applications in their natural state because of the shrink-swell phenomenon. Numerous stabilizers and methods have been used to stabilize expansive soils as an effort to make them more practical for construction purposes. Searching for suitable soil stabilizers to overcome difficulties caused by the expansive soils is the key issue, not only in terms of achieving the required soil geotechnical characteristics but also in terms of environmental and economic concerns. The purpose of this article was to assess the current trends, challenges, and opportunities of various admixtures utilized for expansive soil improvement, as well as their economic and environmental consequences. A critical review of various admixtures commonly used as soil stabilizers, including marble waste powder, fly ash, eggshell powders, stone waste, and lime powder is conducted. Furthermore, a the review was also focused to analyze the offered stabilizers in terms of soil geotechnical properties and sustainability in the field application.

Land use/land cover (LULC) indices can be considered while developing land surface temperature (LST) models. The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes. This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon. Artificial neural network (ANN) and gene expression programming (GEP) were employed to develop LST prediction models. Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models. The extracted data of LULC indices, such as normal difference vegetation index (NDVI), normal difference built-up index (NDBI), urban index (UI), and normal difference water index (NDWI), were utilized as attributes to model LST. The results show that the root-mean-square error (RMSE) of the ANN and GEP models were 0.91oC and 1.08 oC, respectively. The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation, which can be conveniently used to test new data regarding the thematic area. The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI, NDVI, and NDWI towards contributing LST.

GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline conditions. While investigating the durability of GFRP bars in alkaline environment, the effect of surrounding temperature and conditioning duration on tensile strength retention (TSR) of GFRP bars is well investigated with laboratory aging of GFRP bars. However, the role of variable bar size and volume fraction of fiber have been poorly investigated. Additionally, various structural codes recommend the use of an additional environmental reduction factor to accurately reflect the long-term performance of GFRP bars in harsh environments. This study presents the development of Random Forest (RF) regression model to predict the TSR of laboratory conditioned bars in alkaline environment based on a reliable database comprising 772 tested specimens. RF model was optimized, trained, and validated using variety of statistical checks available in the literature. The developed RF model was used for the sensitivity and parametric analysis. Moreover, the formulated RF model was used for studying the long-term performance of GFRP rebars in the alkaline concrete environment. The sensitivity analysis exhibited that temperature and pH are among the most influential attributes in TSR, followed by volume fraction of fibers, duration of conditioning, and diameter of the bars, respectively. The bars with larger diameter and high-volume fraction of fibers are less susceptible to degradation in contrast to the small diameter bars and relatively low fiber's volume fraction. Also, the long-term performance revealed that the existing recommendations by various codes regarding environmental reduction factors are conservative and therefore needs revision accordingly.

In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit wL, plastic limit wP, plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content wopt) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.

Abstract Current study compares the Therapeutic/nutra-pharmaceuticals potential and phenolics profile of Pakistani grown Pakistani and Chinese varieties of ginger. Crude yield of bioactive components from the varieties tested, using different extraction solvents including chloroform, ethyl acetate, ether, methanol, ethanol and distilled water. The crude bioactives varied from 14.1-82.5%. The highest extraction yield was noted for Pakistani species. The HPLC analysis revalued significant amounts of phenolics including vanillin, protocatechuic, vanillic, ferulic, sinapinic and cinnamic acids. The highest anti-inflammatory activity was shown by ethanolic extract of Pakistani variety (IC50: 26.5±1.8) whereas Chinese variety exhibited potent anticancer potential against MCF-7 cell line (Inhibition: 91.38 %). The Chinese variety in general showed higher phenolics and anticancer, while the Pakistani exhibited higher anti-inflammatory activity. Pakistani grown ginger and ethanolic extract of Chinese ginger showed highest antimicrobial activity against Pseudomonas aeruginosa 18.0±0.02 & 15.00±0.02 mm respectively. Minimum results obtained with water for both varieties of ginger with range of 7.2±0.22 and 6±0.07 respectively. Moreover, the phenolics composition, anti-inflammatory, antibacterial and anticancer activities of both tested varieties of ginger were notably affected as a function of extraction solvents. Our findings advocate selection of appropriate solvent for recovery of effective phenolic bioactive compounds from ginger verities to support the Nutra-pharmaceutical formulation.

The focus of this experimental study is to ameliorate the engineering behavior of Palygorskite-rich medium Expansive soils (ES) by utilizing soda lime glass powder (SLGP). The hydrophilic ES are problematic and tend to damage the pavements, boundary walls, and slab-on-grade members Whereas, the accumulation of huge amounts of SLGP is responsible for multitude of environmental issues. Therefore, SLGP was added to the ES using various dosage levels (0 %, 6 %, 10 %, 14 %, 16 %, 18 %, and 22 % by dry weight of the ES to assess the compaction, consolidation and strength characteristics by performing geotechnical laboratory tests. Also, the effect of curing on th unconfined compressive strength of ES (UCS-ES) after 3, 7, 14, and 28 days was studied by considering the mineralogical changes. The microscopic mechanisms of the virgin ES and the SLGP-treated mixtures was evaluated by X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analyses. The results revealed that, plasticity dropped by almost 80 % (due to filler effect, cohesionless character, and higher silica amount of the SLGP), MDD increased from 18.25 to 19.16 kN/m3, and the OMC lowered down (due to the reduction in volume of clay minerals and interlayer spaces). With increasing SLGP dosage levels the coefficient of compression (Cc) decreased by 42.86 %, coefficient of volume (mv) by 60 %, coefficient of consolidation (cv) by 91.53 %, and the pre-consolidation pressures (Pc) witnessed an increase of 240 %. The UCS-ESunsoaked values exceed than the UCS-ES soaked values, particularly at early days of curing (1–7 days) such that UCS-ESsoaked = 13 × UCS-ESsoaked after one day, whereas UCS-ESsoaked = 5.25 × UCS-ESsoaked after 28 days. Similarly, a remarkable improvement (of six -fold) in strength was recorded based on California bearing ratio (CBR) values with 16 % replacement of the SLGP additive. Also, the compactability and strength characteristics were significantly improved and the plasticity plummeted due to formation of C-S-H and C-A-H compounds. Thus, the inclusion of 16 % SLGP in the medium ES was found as the optimum amount in reducing plasticity, improving consolidation characteristics and imparting strength.

The structural failures induced by alkali-contamination of soils are well established by earlier researchers. In the present study, the effect of alkali concentration (2 M and 4 M) and duration of curing on the unconfined compressive strength (UCS) of two types of soils (Kaolin and BC soil) were evaluated. Further, the effect of fly ash (FA) addition (10%, 15%, and 20%) on the UCS of alkali-contaminated soils was also determined. The results revealed that alkali contamination resulted in reducing UCS (by 36% and 46%), observed for kaolin and BC soil, respectively. The addition of FA has resulted in a linear increase in UCS of both soils. The optimum content was noted at 15%, attributed to the pozzolanic reaction initiated by alkali and subsequent gain of UCS with the formation of calcium (with FA addition) based hydration compounds. In addition, three attributes, i.e., FA content, curing period, and alkali concentration, have been used to develop GEP tree-based models to furnish simple prediction equations for calculating the UCS of Kaolin and BC soils. The accuracy of the developed models were initially validated using various statistical checks such as R, MAE, RMSE and RSE. For the 2nd level validation, parametric and sensitivity analyses were carried out for both soils, which yielded a comparable variation and contribution of each input conforming to the literature.

Abstract Chemotherapy appeared to be a significant advancement in cancer research, with fewer side effects. Methotrexate (MTX) is a widely used anticancer drug with strong activity but serious side effects. Several MTX derivatives have been reported, with modifications at various sites to reduce side effects and increase efficacy. The current study uses FTIR spectroscopy to predict the survival fraction of human malignant glioma U87 (MG-U87) cell lines against MTX derivatives. Together with Parent MTX several aldehydes viz. Benzaldehyde, Chlorobenzaldehyde, 2-Chlorobenzaldehyde, 3-Nitrobenzaldehyde, 5-Chloro-2-hydroxybenz-aldehyde, 2-Hydroxy-5-Nitrobenzaldehyde, 2-Thiocarboxyaldehyde, Trans-2-pentenal, and Glutaraldehyde are treated with MTX to obtain MTX derivatives. The prediction of survival fraction of malignant glioma cells is carried out by Lasso, Elastic net and Soft PLS at different concentration levels of synthesized derivatives, including 400 μM, 200 μM, 100 μM, 50 μM, 25 μM and 12.5 μM. The cross-validated prediction error is minimised to optimise spectral wavelength selection and model parameters. It appears that the RMSE computed from test data is significantly varying with the change of models (p = 0.012), with the change of concentrations levels (p $$\le 0.001$$ ≤ 0.001 ) and with the change of combination of models and concentration level (p $$\le 0.001$$ ≤ 0.001 ). StPLS outperforms in predicting survival fraction of glioma cells at the concentration level 50 μM, 100 μM and 400 μM respectively with relative RMSE = 0.1,0.14 and 0.55. Lasso outperforms at the concentration level 12.5 μM, and 200 μM respectively with relative RMSE = 0.4 and 0.14. Elastic net outperforms at the concentration level 25 μM with relative RMSE = 0.8. Consistently appeared influential wavelength identifies the influential functional compounds which best predicts the survival fraction. Hence FTIR appears potential candidate for estimating survival fraction of MTX derivatives.

Abstract Imidazole has anti-inflammatory, antituberculotic, antimicrobial, antimycotic, antiviral, and antitumor properties in the human body, to name a few. Metronidazole [1-(2-Hydroxyethyl)-2-methyl-5-nitroimidazole] is a widely used antiprotozoan and antibacterial medication. Using fourier transform infrared spectroscopy, the current study models the antibacterial activity of already synthesised Metronidazole (MTZ) complexes ( $$MTZ-Benz$$ M T Z - B e n z , $$MTZ-Benz-Cu$$ M T Z - B e n z - C u , $$MTZ-Benz-Cu-Cl_2CHCOOH$$ M T Z - B e n z - C u - C l 2 C H C O O H , $$MTZ$$ MTZ , $$MTZ-Cu$$ M T Z - C u , $$MTZ-Cu-Cl_2CHCOOH$$ M T Z - C u - C l 2 C H C O O H , $$MTZ-Benz-Ag$$ M T Z - B e n z - A g , $$MTZ-Benz-Ag-Cl_2CHCOOH$$ M T Z - B e n z - A g - C l 2 C H C O O H , $$MTZ-Ag$$ M T Z - A g and $$MTZ-Ag-Cl_2CHCOOH$$ M T Z - A g - C l 2 C H C O O H ) against E. coli, B. bronceptica, S. epidermidis, B. pumilus and S. aureus. To characterise the Metronidazole complexes for antibacterial activity against 05 microbes, the least angular regression and least absolute shrinkage selection operators were used. Asymmetric Least Squares was used to correct the spectrum baseline. Least angular regression outperforms cross-validated root mean square error in the fitted models. Using Least angular regression, influential wavelengths that explain the variation in antibacterial activity of Metronidazole complexes were identified and mapped against functional groups.

Poly (l-lacticacid) (PLLA) is an FDA approved material for bone tissue engineering but its inherent brittleness and low melting ability are major challenges for its large-scale commercial applications. This challenge can be overcome by making its blends. This study reports blends of PLLA with a relatively new class of polymer i.e. cyclic olefinic copolymer and their miscibility, thermal behavior, morphology, crystallinity, degradability, and excellent biocompatibility is evaluated. Blends were prepared by taking 5–30 weight percent COC with PLLA matrix using solvent casting method. Scanning electron microscopy (SEM) images revealed that PLLA/COC form miscible blends up to 5–20 wt% of COC. FTIR, XRD, and differential scanning calorimetry (DSC) data showed that addition of COC to PLLA resulted in decrease in crystallinity along with formation of new α′-crystalline phase which coexists with inherent α-phase of PLLA in the blends. The transformation of α′-form is due to the presence of Van der Waals forces of interactions of the polymer chain moieties between PLLA and COC. Interestingly, PLLA/COC blends exhibited superior mechanical properties in relation to the pure PLLA. Compressive modulus values for PLLA/COC 10wt% increased 117% as compared to pure PLLA. PLLA/COC blends at 10 wt% have maximum ultimate tensile strength, modulus, and toughness ~ 123%, 67.8% and, 18.87% respectively. PLLA/COC blends showed increased swelling and degradation results as compared to PLLA. PLLA/COC blends exhibited excellent cytocompatibility over PLLA with preosteoblast (MC3T3-E1) and bone marrow stem cells (BMSc) cell lines suggesting possible candidate for Bone Tissue Engineering.

Synthesis and Characterization of six novel N,N-dioctylsuccinamate based room temperature ionic liquids (RTILs) bearing imidazolium, pyridinium, ester imidazolium, and quaternary ammonium cations is reported. Extraction of Pb(II) and Co(II) by these RTILs has been investigated. Ionic liquids (ILs) synthesized were [C4mim][N88SA], [C8mim][N88SA], [C4Py][N88SA], [C8Py][N88SA], [α-mim-ester][N88SA] and [N2244][N88SA] termed as L1, L2, L3, L4, L5 and L6 respectively. Liquid-liquid extraction was performed and all the six systems showed excellent extractability results for both Pb(II) and Co(II). During the process of extraction several factors i.e., nature of cation, pH of the aqueous phase, equilibration time, and initial metal ion concentration were investigated. The extraction efficiency of above 98 % for all types of extractants was observed. The nature of cation its concentration, equilibration time, and pH of the aqueous phase significantly influenced the extraction efficiency. Maximum extraction was observed at pH values between 4 and 8 and optimum contact time was observed to be 40–45 min. Increasing the metal ion concentration decreased the extraction efficiency. The extraction efficiency of both metal ions decreased in the order [N88SA][C8mim] (L2) > [α-mim-Ester][N88SA] (L5) > [N88SA][C4mim] (L1). This is evident from the order of extraction behaviour that increasing the bulkiness of cation, results in stronger complexation, hence increasing extraction.

Novel ultrafiltration (UF) polymer membranes were prepared to enhance the antifouling features and filtration performance. Several ultrafiltration polymer membranes were prepared by incorporating different concentrations of water-soluble cationic poly [2-(dimethyl amino) ethyl methacrylate] (PDMAEMA) into a homogenous casting solution of polyethersulfone (PES). After adding PDMAEMA, the effects on morphology, hydrophilicity, thermal stability, mechanical strength, antifouling characteristics, and filtration performance of these altered blended membranes were investigated. It was observed that increasing the quantity of PDMAEMA in PES membranes in turn enhanced surface energy, hydrophilicity, and porosity of the membranes. These new modified PES membranes, after the addition of PDMAEMA, showed better filtration performance by having increased water flux and a higher flux recovery ratio (FRR%) when compared with neat PES membranes. For the PES/PDMAEMA membrane, pure water flux with 3.0 wt.% PDMAEMA and 0.2 MPa pressure was observed as (330.39 L·m−2·h−1), which is much higher than that of the neat PES membrane with the value of (163.158 L·m−2·h−1) under the same conditions. Furthermore, the inclusion of PDMAEMA enhanced the antifouling capabilities of PES membranes. The total fouling ratio (TFR) of the fabricated PES/PDMAEMA membranes with 3.0 wt.% PDMAEMA at 0.2 MPa applied pressure was 36 percent, compared to 64.9 percent for PES membranes.

The rising demand for coastal infrastructure has led to seawater and sea sand concrete which is highly alkaline in nature. Glass-fibre reinforced polymer (GFRP) bars are a durable alternative to conventional steel rebars; however, they are susceptible to degradation in a harsh alkaline concrete environment. This study aims to develop three soft computing techniques, namely minimax probability machine regression (MPMR), deep neural network (DNN), and integrated adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), to estimate accurate tensile strength retention (TSR) of conditioned GFRP rebars in the aggressive alkaline concrete environment. The sensitivity of production parameters of GFRP rebars and environmental factors in estimating TSR is also discussed. Statistical analyses, e.g. root mean square error (RMSE) and determination coefficient (R2), are used to assess the proposed models' accuracy. The predictions from the developed models manifested a reliable agreement to the experimental results.In comparison, the developed DNN and hybrid ANFIS-GA algorithms outperformed MPMR. The RMSE and R2 are (8.16% and 78.22), (7.42% and 76.04), and (7.53% and 75.00) for the ANFIS-GA, DNN, and MPMR, respectively. The DNN and ANFIS-GA were also compared with the previous approaches developed in the literature. The superior performance of DNN and ANFIS-GA was observed with a maximum model error approach of 10.71%. Furthermore, the sensitivity of temperature and pH of the conditioning environment significantly affect the TSR of GFRP bars. The diameter of GFRP bars and volume fraction of fibres are essential attributes in the degradation of GFRP rebars and shall be considered in the design of concrete structures.

The use of fiber-reinforced polymer (FRP) bars to overcome the corrosion problems in various reinforced concrete structures is now well documented in the literature. As a result, the currently available design guidelines such as North American design codes allow for using the FRP bars as alternative materials to steel bars to be incorporated into the concrete structures. In practice, hollow-core concrete columns (HCCs) are widely accepted to make a lightweight structure and reduce its cost. Due to the lack of laboratory tests, engineers may not perform a safe design of HCCs with internal FRP bars. Therefore, the presented paper has endeavored to numerically and theoretically explore using the FRP bars and spirals as internal reinforcement for HCCs and investigate the effects of several test parameters. Using the current version of Finite Element Analysis (FEA) ABAQUS (3DS, 2014), a total of 116 HCCs were simulated based on 29 specimens experimentally tested by the researchers which acted as control specimens for the FE model. The complex structural response of concrete was reasonably determined using the concrete damaged plasticity model (CDPM) and the mechanical response of the FRP rebars are considered to behave linearly up to failure with no yielding stage. The calibrated FE model can provide an excellent portrayal of the HCCs’ response. Based on the database obtained from laboratory and simulation, several Artificial Neural Network (ANN) models were further provided to predict the confined compressive load of GFRP-RC HCCs at different loading stages.

Antibacterial activity is an essential property of ionic liquids. In this work, a comprehensive study has been performed on the antibacterial activity of ionic liquids to be utilized for further research and applications. Eighteen ionic liquids viz. Octyl Imidazolium, octyl Pyridinium, quaternary phosphonium-based cations containing bromide, sodium methane sulphonates, bis(trifluoromethane sulfonyl) imide, dichloroacetate, tetrafluoroborate, hydrogen sulfate were prepared and characterized with the help of different spectroscopic techniques. All these samples of ionic liquids were tested for their antibacterial activity against the most commonly occurring bacteria in the environment, i.e., Enterobacter aerogenes (E. aerogenes), Proteus vulgaris (P. vulgaris), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), Escherichia coli (E. coli), and Streptococcus pyogenes (S. pyogenes). Most of the ionic liquids show good antibacterial properties, and imidazolium-based ionic liquids were even more antibacterial as compared to positive control. It was observed that a unique combination of cation and anion is essential to achieve desired antibacterial properties. The mechanism of antibacterial activity was further investigated using density functional theory calculations. A good correlation was found between experimental and theoretical studies.

Abstract Various strains of the mycoparasitic fungal species Clonostachys rosea are used commercially as biological control agents for the control of fungal plant diseases in agricultural crop production. Further improvements of the use and efficacy of C. rosea in biocontrol require a mechanistic understanding of the factors that determines the outcome of the interaction between C. rosea and plant pathogenic fungi. Here, we determined the genome sequences of 11 Clonostachys strains, representing five species in Clonostachys subgenus Bionectria, and performed a comparative genomic analysis with the aim to identify gene families evolving under selection for gene gains or losses. Several gene families predicted to encode proteins involved in biosynthesis of secondary metabolites, including polyketide synthases, nonribosomal peptide syntethases and cytochrome P450s, evolved under selection for gene gains (p ≤ .05) in the Bionectria subgenus lineage. This was accompanied with gene copy number increases (p ≤ .05) in ATP‐binding cassette (ABC) transporters and major facilitator superfamily (MFS) transporters predicted to contribute to drug efflux. Most Clonostachys species were also characterized by high numbers of auxiliary activity (AA) family 9 lytic polysaccharide monooxygenases, AA3 glucose–methanol–choline oxidoreductases and additional carbohydrate‐active enzyme gene families with putative activity (or binding) towards xylan and rhamnose/pectin substrates. Particular features of the C. rosea genome included expansions (p ≤ .05) of the ABC‐B4 multidrug resistance transporters, the ABC‐C5 multidrug resistance‐related transporters and the 2.A.1.3 drug:H + antiporter‐2 MFS drug resistance transporters. The ABC‐G1 pleiotropic drug resistance transporter gene abcG6 in C. rosea was induced (p ≤ .009) by exposure to the antifungal Fusarium mycotoxin zearalenone (1121‐fold) and various fungicides. Deletion of abcG6 resulted in mutants with reduced (p

Abstract Biological control is a promising approach to reduce plant diseases caused by nematodes to ensure high productivity in agricultural production. Large‐scale analyses of genetic variation in fungal species used for biocontrol can generate knowledge regarding interaction mechanisms that can improve efficacy of biocontrol applications. In this study, we performed a genome‐wide association study (GWAS) for in vitro antagonism against the root lesion nematode Pratylenchus penetrans in 53 previously genome re‐sequenced strains of the biocontrol fungus Clonostachys rosea. Nematode mortality in C. rosea potato dextrose broth (PDB) culture filtrates was highly variable and showed continuous variation (p

Objective: Our research article aimed to determine if six-month mortality amongst hepatitis B and C patients undergoing cardiac surgery varied according to gender, post-operatively. Secondarily, we highlighted the significant differences among the two genders in their pre-operative, operative, and post-operative characteristics and deduced significant predictors of mortality. Methods: We obtained approval from the International Review Board of the Dow University of Health Sciences, and conducted a retrospective study targeting hepatitis B and C patients who had undergone cardiac surgery between January 2013 to October 2018 at the Ruth Pfau Civil Hospital, Karachi, Pakistan. The data was analysed using the Statistical Package for Social Sciences (Version 20.0). The population was divided into two groups, based on gender. Chi-squared test was used to compare categorical variables and odd ratios with 95% confidence interval were also computed. Differences in continuous variables were assessed using independent T-test or Mann-Whitney U test. Results: There was no significant difference in six-month mortality between the genders, with a 22.5% mortality in males and 20.0% mortality in females. Post-operatively, males had higher creatinine (p=0.003) levels but females tended to have a longer ward stay (p=0.032). On multivariate logistic regression, duration of intubation (aOR=1.131, 95% CI: 1.002-1.275), cardiopulmonary bypass time (aOR=1.030, 95% CI: 1.002-1.059) and duration of ward stay (aOR=1.100, 95% CI: 1.031-1.175) were found to be significant predictors of mortality. Conclusion: There is no association between six-month mortality and gender among hepatitis B and C patients undergoing cardiac surgery. Additionally, duration of intubation, cardiopulmonary bypass time and duration of ward stay are significant predictors of sixmonth mortality

Abstract Amine-terminated core-shell silica coated magnetite nanoparticles were functionalized with diglycolic acid for the first time to create acid moiety on the surface of the nanoparticles. The formation of magnetite nanoparticles was scrutinised through XRD, SEM, EDS, TEM, VSM and FTIR spectroscopy. The BET surface area of nano-sorbent was found to be 4.04 m2/g with pore size 23.68 nm. These nanomaterials were then utilized to remove the Pb(II) and Cr(VI) ions from their aqueous media and uptake of metal ions was determined by atomic absorption spectroscopy (AAS). A batch adsorption technique was applied to remove both ions at optimised pH and contact time with maximum adsorption efficiency for Pb(II) ions at pH 7 while for Cr(VI) ions at pH 3. Adsorption mechanism was studied using Langmuir and Freundlich isotherms and equilibrium data fitted well for both the isotherms, showing complex nature of adsorption comprising both chemisorption as well as physio-sorption phenomena. The nanosorbents exhibited facile separation by applying external magnetic field due to the ferrimagnetic behaviour with 31.65 emu/g saturation magnetization. These nanosorbents were also found to be used multiple times after regeneration.

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R2, RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R2 value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R2 value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R2 value (0.89) compared with that of the GEP model (R2 = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods—namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models—based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

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 (Pu) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP). A total of 149 datapoints were obtained from the literature, considering ten input parameters, including the outer diameter of steel tube (D), wall thickness of steel tube, compressive strength of concrete (fc’), elastic modulus of concrete (Ec), yield strength of steel (fv), elastic modulus of steel (Es), length of the column (L), confinement factor (ζ), ratio of D to thickness of column, and the ratio of length to D of column. The performance of the developed models was assessed using coefficient of regression R2, root mean squared error RMSE, mean absolute error MAE and comparison of regression slopes. It was found that the optimal GEP Model T3, having number of chromosomes Nc = 100, head size Hs = 8 and number of genes Ng = 3, outperformed all the other models. For this particular model, R2overall equaled 0.99, RMSE values were 133.4 and 162.2, and MAE = 92.4 and 108.7, for training (TR) and testing (TS) phases, respectively. Similarly, the comparison of regression slopes analysis revealed that the Model T3 exhibited the highest R2 of 0.99 with m = 1, in both the TR and TS stages, respectively. Finally, parametric analysis showed that the Pu of composite steel columns increased linearly with the value of D, t and fy.

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. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.

Ionic liquids (ILs) have emerged as active pharmaceutical ingredients because of their excellent antibacterial and biological activities. Herein, we used the green-chemistry-synthesis procedure, also known as the metathesis method, to develop three series of ionic liquids using 1-methyl-3-butyl imidazolium, butyl pyridinium, and diethyldibutylammonium as cations, and bromide (Br−), methanesulfonate (CH3SO3−), bis(trifluoromethanesulfonyl)imide (NTf2−), dichloroacetate (CHCl2CO2−), tetrafluoroborate (BF4−), and hydrogen sulfate (HSO4−) as anions. Spectroscopic methods were used to validate the structures of the lab-synthesized ILs. We performed an agar well diffusion assay by using pathogenic bacteria that cause various infections (Escherichia coli; Enterobacter aerogenes; Klebsiella pneumoniae; Proteus vulgaris; Pseudomonas aeruginosa; Streptococcus pneumoniae; Streptococcus pyogenes) to scrutinize the in vitro antibacterial activity of the ILs. It was established that the nature and unique combination of the cations and anions were responsible for the antibacterial activity of the ILs. Among the tested ionic liquids, the imidazolium cation and NTf2− and HSO4− anions exhibited the highest antibacterial activity. The antibacterial potential was further investigated by in silico studies, and it was observed that bis(trifluoromethanesulfonyl)imide (NTf2−) containing imidazolium and pyridinium ionic liquids showed the maximum inhibition against the targeted bacterial strains and could be utilized in antibiotics. These antibacterial activities float the ILs as a promising alternative to the existing antibiotics and antiseptics.

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN models, a dataset of 149 experimental tests was initially gathered from the accessible literature. Eight PCA-based hybrid ANNs were created using eight MOAs, including artificial bee colony, ant lion optimization, biogeography-based optimization, differential evolution, genetic algorithm, grey wolf optimizer, moth flame optimization and particle swarm optimization. The created ANNs’ performance was then assessed. With R2 ranges between 0.7094 and 0.9667 in the training phase and between 0.6883 and 0.9634 in the testing phase, we discovered that the accuracy of the built hybrid models was good. Based on the outcomes of the experiments, the generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, with R2 = 0.9667 and 0.9634. The created ANN-GWO may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings.

The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%).

This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.

The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R2 (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R2 of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL.

Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70% of the data was used for training the model and remaining 30% was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations.

The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were constructed. The performance of the developed SVMs was then evaluated. The accuracy of the constructed hybrid models was found to be on the higher side, with R2 ranges between 0.8870 and 0.9774 in the training phase and between 0.8270 and 0.9294 in the testing phase. Based on the experimental results, the developed SVM–HHO (a hybrid model that uses an SVM and the Harris hawks optimization) was overall the most accurate model, with R2 values of 0.9241 and 0.9241 in the training and testing phases, respectively. Experimental results also demonstrate that the developed hybrid SVM can be used as an alternate tool for estimating the ultimate IBS capacity of FRP concrete in civil engineering projects.

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., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature.

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. The effect of incorporating different dosages of FA (10%, 15%, and 20%) on the UCSkaolin and UCSBC soils was also studied. Sufficient laboratory test data comprising 384 data points were collected, and multi expression programming (MEP) was used to create tree-based models for yielding simple prediction equations to compute the UCSkaolin and UCSBC soils. The experimental results reflected that alkali contamination resulted in reduced UCS (36% and 46%, respectively) for the kaolin and BC soil, whereas the addition of FA resulted in a linear rise in the UCS. The optimal dosage was found to be 20%, and the increase in UCS may be attributed to the alkali-induced pozzolanic reaction and subsequent gain of the UCS due to the formation of calcium-based hydration compounds (with FA addition). Furthermore, the developed models showed reliable performance in the training and validation stages in terms of regression slopes, R, MAE, RMSE, and RSE indices. Models were also validated using parametric and sensitivity analysis which yielded comparable variation while the contribution of each input was consistent with the available literature.

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. The models were trained using optimized hyperparameters obtained from the trial-and-error method. The coefficient of correlation (R), Mean Absolute Error, and Root Mean Square Error (RMSE) were observed as 0.99, 5.67 kN-m, and 7.37 kN-m, for ANN, while 0.97, 7.63 kN-m, and 8.02 kN-m for RF regression model, respectively. Both models showed close agreement between experimental and predicted results; however, the ANN model showed superior accuracy and flexural strength performance. The parametric and sensitivity analysis of the ANN models showed that an increase in bottom reinforcement, width and depth of the beam, and increase in compressive strength increased the bending moment capacity of the beam, which shows the predictions by the model are corroborated with the literature. The sensitivity analysis showed that variation in bottom flexural reinforcement is the most influential parameter in yielding flexural capacity, followed by the overall depth and width of the beam. The change in elastic modulus and ultimate strength of FRP manifested the least importance in contributing flexural capacity.

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. For this purpose, a number of wet–dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

The fouling of surfaces such as textiles is a major health challenge, and there is a continuous effort to develop materials and processes to overcome it. In consideration of this, this study regards the development of antifouling functional nanoencapsulated finishing for the cotton textile fabric by employing a layer-by-layer dip coating technique. Antifouling textile finishing was formulated by inducing the nanoencapsulation of the antifouling functional group inside the hydrophobic polymeric shell. Cotton fabric was taken as a substrate to incorporate antibacterial functionality by alternatively fabricating multilayers of antifouling polymeric formulation (APF) and polyelectrolyte solution. The surface morphology of nanoencapsulated finished textile fabric was characterized through scanning electron microscopy to confirm the uniform distribution of nanoparticles on the cotton textile fabric. Optical profilometry and atomic force microscopy studies indicated increased surface roughness in the coated textile substrate as compared to the uncoated textile. The surface thickness of the fabricated textile increased with the number of deposited bilayers on the textile substrate. Surface hydrophobicity increased with number of coating bilayers with θ values of x for single layer, up to y for 20 bilayers. The antibacterial activity of the uncoated and layer-by-layer coated finished textile was also evaluated. It was significant and exhibited a significant zone of inhibition against microbial strains Gram-positive S. aureus and Gram-negative E. coli. The bilayer coating exhibited water repellency, hydrophobicity, and antibacterial activity. Thus, the fabricated textile could be highly useful for many industrial and biomedical applications.

Reinforced concrete structures are subjected to frequent maintenance and repairs due to steel reinforcement corrosion. Fiber-reinforced polymer (FRP) laminates are widely used for retrofitting beams, columns, joints, and slabs. This study investigated the non-linear capability of artificial intelligence (AI)-based gene expression programming (GEP) modelling to develop a mathematical relationship for estimating the interfacial bond strength (IBS) of FRP laminates on a concrete prism with grooves. The model was based on five input parameters, namely axial stiffness (Eftf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of the groove (hg), and IBS was considered the target variable. Ten trials were conducted based on varying genetic parameters, namely the number of chromosomes, head size, and number of genes. The performance of the models was evaluated using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The genetic variation revealed that optimum performance was obtained for 30 chromosomes, 11 head sizes, and 4 genes. The values of R, MAE, and RMSE were observed as 0.967, 0.782 kN, and 1.049 kN for training and 0.961, 1.027 kN, and 1.354 kN. The developed model reflected close agreement between experimental and predicted results. This implies that the developed mathematical equation was reliable in estimating IBS based on the available properties of FRPs. The sensitivity and parametric analysis showed that the axial stiffness and width of FRP are the most influential parameters in contributing to IBS.

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. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

Much research has been conducted and published on the examination of the behavior of reinforced steel and concrete structures with a FRP system. Nevertheless, the performance of FRP differs from that of FRCM, particularly at high temperature and ultimate strength. The present study provides a review of previous research on structural elements (viz. beams, columns, arches, slabs, and walls) retrofitted with FRCM systems, taking account of various parameters, such as layers, composite types, configurations, and anchors for controlling or delaying failure modes (FMs). Additionally, this paper discussed the details of different FMs observed during experimental tests, such as crushed concrete or bricks, fiber debonding from substrate materials, slippage, fiber rupture, and telescopic failure for strengthened specimens. Moreover, this paper investigated where and how fractures may develop in structural elements retrofitted with the FRCM system under various retrofit scenarios. To this end, in addition to the review of the relevant literature, a large dataset has been compiled from different (RC) structural elements and masonry members. Next, a relationship is developed between failure modes (FMS) and influential parameters, i.e., the number of layers and the type of composite, based on this dataset. This can be used as a benchmark example in future studies, as there is no such basis available in the literature, to the best of the authors’ knowledge.