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 (PsUCS-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 PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-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 (R2), 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 Ps-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 PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Determinants of pharmacists' confidence to participate in antimicrobial stewardship: A confirmatory factor analysis.

AIM: Pharmacists are essential members of hospital antimicrobial stewardship (AMS) teams. A lack of self-perceived confidence can limit pharmacists' involvement and contributions. Pharmacists working in AMS have reported a lack of confidence. There is currently a lack of validated measures to assess pharmacists' self-perceived confidence when working in AMS and contributors to this confidence. This study aimed to identify variables contributing to pharmacist self-perceived confidence and validate an AMS hospital pharmacist survey tool using confirmatory factor analysis (CFA). METHODS: Responses from a survey of Australian and French hospital pharmacists were used to undertake CFA and path analysis on factors related to pharmacists' self-perceived confidence. It was hypothesized that pharmacists' self-perceived confidence would be impacted by time working in AMS, perceived importance of AMS programmes, perceived barriers to participating in AMS and current participation. RESULTS: CFA demonstrated a good model fit between the factors. Items included in the model loaded well to their respective factors with acceptable reliability. Path analysis demonstrated that time working in AMS had a significant impact on pharmacists' self-perceived confidence, while perceived barriers had a negatively significant relationship. Pharmacy participation in AMS and perceived importance of AMS programmes had a non-significant impact. CONCLUSION: Findings demonstrated that the survey tool showed good validity and identified factors that can impact pharmacists' self-perceived confidence when working in hospital AMS programmes. Having a validated survey tool can identify factors that can reduce pharmacists' self-perceived confidence. Strategies can then be developed to address these factors and subsequently improve pharmacists' self-perceived confidence.

Punicalagin, a pomegranate polyphenol sensitizes the activity of antibiotics against three MDR pathogens of the Enterobacteriaceae.

BACKGROUND: Multidrug resistance (MDR) in the family Enterobacteriaceae is a perniciously increasing threat to global health security. The discovery of new antimicrobials having the reversing drug resistance potential may contribute to augment and revive the antibiotic arsenal in hand. This study aimed to explore the anti-Enterobacteriaceae capability of bioactive polyphenols from Punica granatum (P. granatum) and their co-action with antibiotics against clinical isolates of Enterobacteriaceae predominantly prevalent in South Asian countries. METHODS: The Kandhari P. granatum (Pakistani origin) extracts were tested for anti-Enterobacteriaceae activity by agar well diffusion assay against MDR Salmonella enterica serovar Typhi, serovar Typhimurium and Escherichia coli. Predominant compounds of active extract were determined by mass spectrometry and screened for bioactivity by agar well diffusion and minimum inhibitory concentration (MIC) assay. The active punicalagin was further evaluated at sub-inhibitory concentrations (SICs) for coactivity with nine conventional antimicrobials using a disc diffusion assay followed by time-kill experiments that proceeded with SICs of punicalagin and antimicrobials. RESULTS: Among all P. granatum crude extracts, pomegranate peel methanol extract showed the largest inhibition zones of 25, 22 and 19 mm, and the MICs as 3.9, 7.8 and 7.8 mg/mL for S. typhi, S. typhimurium and E. coli, respectively. Punicalagin and ellagic acid were determined as predominant compounds by mass spectrometry. In plate assay, punicalagin (10 mg/mL) was active with hazy inhibition zones of 17, 14, and 13 mm against S. typhi, S. typhimurium and E. coli, respectively. However, in broth dilution assay punicalagin showed no MIC up to 10 mg/mL. The SICs 30 µg, 100 µg, and 500 µg of punicalagin combined with antimicrobials i.e., aminoglycoside, ß-lactam, and fluoroquinolone act in synergy against MDR strains with % increase in inhibition zone values varying from 3.4 ± 2.7% to 73.8 ± 8.4%. In time-kill curves, a significant decrease in cell density was observed with the SICs of antimicrobials/punicalagin (0.03-60 µg/mL/30, 100, 500 µg/mL of punicalagin) combinations. CONCLUSIONS: The P. granatum peel methanol extract exhibited antimicrobial activity against MDR Enterobacteriaceae pathogens. Punicalagin, the bacteriostatic flavonoid act as a concentration-dependent sensitizing agent for antimicrobials against Enterobacteriaceae. Our findings for the therapeutic punicalagin-antimicrobial combination prompt further evaluation of punicalagin as a potent activator for drugs, which otherwise remain less or inactive against MDR strains.

The impact of phosphodiesterase inhibition on neurobehavioral outcomes in preclinical models of traumatic and non-traumatic spinal cord injury: a systematic review.

Study design: Systematic review. Objective: The objective of this study was to evaluate the impact of phosphodiesterase (PDE) inhibitors on neurobehavioral outcomes in preclinical models of traumatic and non-traumatic spinal cord injury (SCI). Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and was registered with PROSPERO (CRD42019150639). Searches were performed in MEDLINE and Embase. Studies were included if they evaluated the impact of PDE inhibitors on neurobehavioral outcomes in preclinical models of traumatic or non-traumatic SCI. Data were extracted from relevant studies, including sample characteristics, injury model, and neurobehavioral assessment and outcomes. Risk of bias was assessed using the SYRCLE checklist. Results: The search yielded a total of 1,679 studies, of which 22 met inclusion criteria. Sample sizes ranged from 11 to 144 animals. PDE inhibitors used include rolipram (n = 16), cilostazol (n = 4), roflumilast (n = 1), and PDE4-I (n = 1). The injury models used were traumatic SCI (n = 18), spinal cord ischemia (n = 3), and degenerative cervical myelopathy (n = 1). The most commonly assessed outcome measures were Basso, Beattie, Bresnahan (BBB) locomotor score (n = 13), and grid walking (n = 7). Of the 22 papers that met the final inclusion criteria, 12 showed a significant improvement in neurobehavioral outcomes following the use of PDE inhibitors, four papers had mixed findings and six found PDE inhibitors to be ineffective in improving neurobehavioral recovery following an SCI. Notably, these findings were broadly consistent across different PDE inhibitors and spinal cord injury models. Conclusion: In preclinical models of traumatic and non-traumatic SCI, the administration of PDE inhibitors appeared to be associated with statistically significant improvements in neurobehavioral outcomes in a majority of included studies. However, the evidence was inconsistent with a high risk of bias. This review provides a foundation to aid the interpretation of subsequent clinical trials of PDE inhibitors in spinal cord injury. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=150639, identifier: CRD42019150639.

Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning.

Precision agricultural techniques try to prevent either an excessive or inadequate application of agrochemicals during pesticide application. In recent years, it has become popular to combine traditional agricultural practices with artificial intelligence algorithms. This research presents a case study of variable-rate targeted spraying using deep learning for tobacco plant recognition and identification in a real tobacco field. An extensive comparison of the detection performance of six YOLO-based models for the tobacco crop has been performed based on experimentation in tobacco fields. An F1-score of 87.2% and a frame per second rate of 67 were achieved using the YOLOv5n model trained on actual field data. Additionally, a novel disturbance-based pressure and flow control method has been introduced to address the issue of unwanted pressure fluctuations that are typically associated with bang-bang control. The quality of spray achieved by attenuation of these disturbances has been evaluated both qualitatively and quantitatively using three different spraying case studies: broadcast, and selective spraying at 20 psi pressure; and variable-rate spraying at pressure varying from 15-120 psi. As compared to the broadcast spraying, the selective and variable rate spray methods have achieved up to 60% reduction of agrochemicals.

Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis.

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 methods­namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models­based 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.

End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

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. 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.

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 (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.

High levels of blood glutamic acid and ornithine in children with intellectual disability.

Objectives: Aminoacidopathies are inborn errors of metabolism (IEMs) that cause intellectual disability in children. Luckily, aminoacidopathies are potentially treatable, if diagnosed earlier in life. The focus of this study was the screening of aminoacidopathies in a cohort of patients suspected for IEMs. Methods: Blood samples from healthy (IQ > 90; n = 391) and intellectually disabled (IQ < 70; n = 409) children (suspected for IEMs) were collected from different areas of Northern Punjab, Pakistan. An analytical HPLC assay was used for the screening of plasma amino acids. Results: All the samples (n = 800) were analyzed on HPLC and forty-three out of 409 patient samples showed abnormal amino acid profiles mainly in the levels of glutamic acid, ornithine and methionine. Plasma concentration (Mean ± SD ng/mL) were significantly high in 40 patients for glutamic acid (patients: 165 ± 38 vs. controls: 57 ± 8, p < 0.00001) and ornithine (patients: 3177 ± 937 vs. controls: 1361 ± 91, p < 0.0001). Moreover, 3 patients showed abnormally high (53.3 ± 8.6 ng/mL) plasma levels of methionine. Conclusion: In conclusion, biochemical analysis of samples from such patients at the metabolites level could reveal the underlying diseases which could be confirmed through advanced biochemical and genetic analyses. Thus, treatment to some of such patients could be offered. Thus burden of intellectual disability caused by such rare metabolic diseases could be reduced from the target populations.

Retraction Note: Screening and identification of bioactive compounds from citrus against non-structural protein 3 protease of hepatitis C virus genotype 3a by fluorescence resonance energy transfer assay and mass spectrometry.

[This retracts the article on p. 976 in vol. 12, PMID: 33312423.].

COVID-19: Vaccine-induced immune thrombotic thrombocytopenia.

In late February 2021, a prothrombotic syndrome was encountered for the first time in some of the recipients of ChAdOx1 CoV-19 vaccine (AstraZeneca, University of Oxford, and Serum Institute of India). Since the hallmark of this syndrome is the development of thrombocytopenia and/or thrombosis between 4 and 42 days after receiving a COVID-19 vaccine, it was named vaccine-induced immune thrombotic thrombocytopenia (VITT). Other names include "vaccine-induced prothrombotic immune thrombocytopenia" and "thrombosis with thrombocytopenia syndrome" by the Centers for Disease Control and the Food and Drug Administration (FDA). VITT appears similar to heparin-induced thrombocytopenia in that "platelet activating" autoantibodies are produced in both these conditions due to prior exposure of COVID-19 vaccine and heparin respectively, in turn causing thrombotic complications and consumptive thrombocytopenia. In this article, recent advances in the understanding of pathobiology, clinical features, investigative work-up, and management of VITT are reviewed.

Towards dealing with commonly occurring requirements engineering process issues during software development outsourcing.

Due to specific advantages, the volume of Software Development Outsourcing (SDO) is rapidly increasing. Because of challenges arising from the Requirements Engineering (RE) process, the anticipated benefits of SDO are not achieved in case of several projects. The objective of this research work is to recommend RE practices for addressing the commonly arising RE process issues in the case of SDO. For this reason, a thorough literature review has been undertaken, as well as two questionnaire surveys have been performed with skilled SDO industry practitioners. The surveys have been done by utilizing semi-supervised style and employing Convenience Sampling method. The 50 percent rule and a four-point Likert Scale have also been used to determine the advantages of RE practices for dealing with the issues. A comprehensive list of 147 RE practices has been extracted by conducting a Focus Group session. Furthermore, the 147 RE practices have been ranked by applying Numerical Assignment and Hundred Dollar Techniques during two Focus Group sessions. The detection and adaptation of RE practices aids in enhancing the SDO RE process, evading SDO failures, and achieving the associated SDO advantages.

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. 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.

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. 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.

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. 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.

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., 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.

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. For the gene expression programming (GEP) approach, a total of 156 samples were prepared in the laboratory using variable percentages of PET and SCM (0−10%, each). To achieve the best hyper parameter setting of the optimized GEP model, 10 trials were undertaken by varying the genetic parameters while observing the models' performance in terms of statistical indices, i.e., correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), comparison of regression slopes, and predicted to experimental ratios (ρ). Sensitivity analysis and parametric study were performed on the best GEP model (obtained at; chromosomes = 50, head size = 9, and genes = 3) to evaluate the effect of contributing input parameters. The sensitivity analysis showed that: CS7d (30.47%) > CS1d (28.89%) > SCM (18.88%) > Flow (18.53%) > PET (3.23%). The finally selected GEP model exhibited optimal statistical indices (R = 0.977 and 0.975, RMSE = 2.423 and 2.531, MAE = 1.918 and 2.055) for training and validation datasets, respectively. The role of PET/SCM has no negative influence on the CS28d of cementitious grouts, which renders the PET a suitable alternative toward achieving sustainable and green concrete. Hence, the simple mathematical expression of GEP is efficacious, which leads to saving time and reducing labor costs of testing in civil engineering projects.

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. 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.

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. The test results indicated that the binary mix with 20% BA and ternary mix with 33% BA and 7% SF exhibited higher strengths than all the other mixes, including the control. The higher strengths of these mixes are also validated by their lower water absorption and apparent porosity than the other mixes. Following mechanical testing, the micro and pore structures of all mixes were investigated by performing scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and nitrogen (N2) adsorption isotherm analysis. In SEM-EDS analysis, a dense and compact microstructure was observed for the BA20 and BA33SF7 mixtures due to the formation of high-density C-S-H and C-H phases. The formation of a large amount of C-S-H phases was observed through FTIR, where a prominent shift in peaks from 955 to 970 cm-1 was observed in the spectra of these mixes. Moreover, in N2 adsorption isotherm analysis, a decrease in the intruded pore volume and an increase in the BET surface area of the paste matrix indicate the densification of the pore structure of these mixes. As observed through TGA, a reduction in the amount of the portlandite phase in these mixes leads to the formation of their more densified micro and pore structures. The current findings indicate that BA (20%) and its blend with SF (40%) represents a potential revenue stream for the development of sustainable and high-performance concretes in the future.