Numerical analysis of pile group, piled raft, and footing using finite element software PLAXIS 2D and GEO5.

Foundation plays a vital role in weight transfer from the superstructure to substructure. However, foundation characteristics such as pile group, piled raft, and footing remain unfolded due to their highly non-linear behaviour in different soil types. Bibliography analysis using VOSvierwer algorithm supported the significance of the research. Hence, this study investigates the load-bearing capacity of different types of foundations, including footings, pile groups, and piled rafts, by analyzing experimental data using finite element tools such as PLAXIS 2D and GEO5. The analysis involves examining the impact of various factors such as the influence of surcharge and the effect of different soil types on the load-bearing capabilities of the different types of foundation. For footing, parametric investigations using PLAXIS 2D are conducted to explore deformational changes. Pile groups are analyzed using GEO5 to assess their factor of safety (FOS.) and settling under various criteria, such as pile length and soil type. The study also provides insight into selecting the right type of foundation for civil engineering practice. Findings showed that different soil types have varying deformational behaviours under high loads with sandy soil having less horizontal deformation than clayey soil. Also, it was observed that increasing the pile thickness by 50% resulted in a reduction of 13.88% in settlement and an improvement of 16.66% in the FOS. In conclusion, this study highlights the importance of professionalism, exceptional talent, and outstanding decision-making when assessing the load-bearing capabilities of various foundation types for building structures.

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models.

Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model.

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models.

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.

Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia.

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI models, and lack in learning/technology transfer. This study explores the extreme gradient boosting (XGBoost) model as a superior SuperLearning (SL) algorithms for Pb prediction. The proposed model was examined using historical data at the Bramble and Deception Bay (BB and DB) stations, Australia. The model was trained at one of the stations and transferred to a cross-station and vice versa. XGBoost showed higher reliability with less declination in (R2: coefficient of determination), i.e., 0.97 % over the testing phase, among others models at BB. At the cross-station (DB), the performance of the XGBoost model was decreased by 2.74 % (R2) against random forests (RF). The mean absolute error (MAE) observed 40 % (XGBoost) and 47 % (RF) less than artificial neural network (ANN). The XGBoost model performance declined by 3.44 % (R2) over testing (DB), which is minor among validated models. At the cross-station (BB), the XGBoost model showed the least decrement in terms of R2, i.e., 7.99 % against the ANN (8.31 %), RF (10.26 %), and support vector machine (SVM, 36.19 %).

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.

Manganese (Mn) removal prediction using extreme gradient model.

Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the development of many attributes of the ecosystem in controlled quantity. In the present paper, an extreme gradient model (XGBoost) is proposed for Mn prediction. A compressive statistical analysis reveals the stochastics behaviour of the data prior to the prediction investigation. The main goal is to determine the Mn predictability of XGBoost algorithm with influencing factors such as D2EHPA (M), Time (min), H2SO4 (M), NaCl (g/L), and EDTA (mM). The PCA biplot signifies the importance of the predictors. The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF). The order of the applied models' performance are XGBoost > RF > SVM > MLR as per their R2 and RMSE metrics over testing phase i.e. 20.88, 0.75, 0.61, 0.40, and 2.23, 3.01, 3.51, 6.38, respectively. Moreover, the Taylor diagram and Radar chart have drown to emphasize the XGBoost model efficiency, stability, and reliability. In respect of XGBoost model prediction, 'Time' predictor outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.

Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media.

Numerous researchers have expressed concern over the emerging water scarcity issues around the globe. Economic water scarcity is severe in the developing countries; thus, the use of inexpensive wastewater treatment strategies can help minimize this issue. An abundant amount of laundry wastewater (LWW) is generated daily and various wastewater treatment researches have been performed to achieve suitable techniques. This study addressed this issue by considering the economic perspective of the treatment technique through the selection of easily available materials. The proposed technique is a combination of locally available absorbent materials such as sand, biochar, and teff straw in a media. Biochar was prepared from eucalyptus wood, teff straw was derived from teff stem, and sand was obtained from indigenous crushed stones. In this study, the range of laundry wastewater flow rate was calculated as 6.23-17.58 m3/day; also studied were the efficiency of the media in terms of the removal percentage of contamination and the flux rate. The performances of biochar and teff straw were assessed based on the operation parameters and the percentage removal efficiency at different flux rates; the assessment showed 0.4 L/min flux rate to exhibit the maximum removal efficiency. Chemical oxygen demand, biological oxygen demand, and total alkalinity removal rate varied from 79% to ≥83%; total solids and total suspended solids showed 92% to ≥99% removal efficiency, while dissolved oxygen, total dissolved solids, pH, and electrical conductivity showed 22% to ≥62% removal efficiency. The optimum range of pH was evaluated between 5.8-7.1. The statistical analysis for finding the correlated matrix of laundry wastewater parameters showed the following correlations: COD (r = -0.84), TS (r = -0.83), and BOD (r = -0.81), while DO exhibited highest negative correlation. This study demonstrated the prospective of LWW treatment using inexpensive materials. The proposed treatment process involved low-cost materials and exhibited efficiency in the removal of contaminants; its operation is simple and can be reproduced in different scenarios.

Comparison of endoscopic ultrasound-guided fine-needle aspiration by capillary action, suction, and no suction methods: a randomized blinded study.

BACKGROUND AND STUDY AIM: Different types of endoscopic ultrasound (EUS)-guided fine-needle aspiration (FNA) techniques are used in clinical practice; the best method in terms of outcome has not been determined. The aim of the study was to compare the diagnostic adequacy of aspirated material, and the cytopathological and EUS morphological features between capillary action, suction, and no-suction FNA methods. PATIENTS AND METHODS: This was a prospective, single-blinded, randomized study conducted at a tertiary care hospital. Patients were randomized to the three groups: capillary action, suction, and no suction. A total of 300 patients were included, with 100 patients in each arm. RESULTS: A total of 300 patients (195 males) underwent EUS-FNA of 235 lymph nodes and 65 pancreatic masses (distribution not statistically different between the groups). The mean age was 52 ±â€Š14 years. A 22 gauge needle was used in the majority (93 %) of procedures. There was no statistical difference between the three groups regarding lymph node size at the largest axis and ratio, type of needle, echo features, echogenicity, calcification, necrosis, shape, borders (lymph nodes), number of passes, and cellularity. Diagnostic adequacy of the specimen was 91 %, 91 %, and 94 % in the capillary, suction, and no suction groups, respectively ( P  = 0.67). Significantly more slides and blood clots were generated by the suction method compared with the other methods. CONCLUSION: The capillary action, suction, and no suction methods of EUS-FNA are similar in terms of diagnostic adequacy of the specimen. The suction method has the disadvantages of causing more bleeding and generating more slides.

Endoscopic ultrasound-guided fine-needle aspiration of the adrenal glands: analysis of 21 patients.

BACKGROUND/AIMS: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) cytology of adrenal masses helps in etiological diagnosis. The aim of this study was to evaluate the diagnostic yield of EUS-FNA of adrenal masses in cases where other imaging methods failed and/or were not feasible. METHODS: Twenty-one consecutive patients with adrenal masses, in whom adrenal FNA was performed because conventional imaging modalities failed and/or were not feasible, were prospectively evaluated over a period of 3 years. RESULTS: Of the 21 patients (mean age, 56±12.2 years; male:female ratio, 2:1), 12 had pyrexia of unknown origin and the other nine underwent evaluation for metastasis. The median lesion size was 2.4×1.6 cm. Ten patients were diagnosed with tuberculosis (shown by the presence of caseating granulomas [n=10] and acid-fast bacilli [n=4]). Two patients had EUS-FNA results suggestive of histoplasmosis. The other patients had metastatic lung carcinoma (n=6), hepatocellular carcinoma (n=1), and adrenal lipoma (n=1) and adrenal myelolipoma (n=1). EUS results were not suggestive of any particular etiology. No procedure-related adverse events occurred. CONCLUSIONS: EUS-FNA is a safe and effective method for evaluating adrenal masses, and it yields diagnosis in cases where tissue diagnosis is impossible or has failed using conventional imaging modalities.

Endoscopic ultrasound-guided pelvic and prostatic abscess drainage: experience in 30 patients.

BACKGROUND: Endoscopic ultrasound (EUS)-guided drainage is an effective treatment for many abscesses in the abdomen. We review our experience with EUS-guided drainage of pelvic abscesses. METHODS: Thirty consecutive patients who underwent EUS-guided pelvic abscess drainage were evaluated after excluding three patients with distance to transducer >2 cm or organized abscess. RESULTS: Thirty patients (25 male) aged 60 ± 4.5 years (mean ± SD) had 4 prostatic abscesses, 7 perisigmoid abscesses, and 19 perirectal abscesses with mean ± SD sizes of 2.5 ± 0.3, 4.7 ± 0.6, and 5.4 ± 0.4 cm, respectively. Surgery was the most common predisposing factor (n = 14, 46.6 %) followed by diverticulitis (n = 5, 16.6 %). Interventions included aspiration only (2 prostatic and 3 perisigmoid), aspiration and dilatation (2 patients in each group), and dilatation and stenting (2 perisigmoid and 17 perirectal). Five (16.6 %) patients needed re-intervention, and two (6.6 %) needed surgery. There were no recurrences. Technical success of EUS-guided pelvic abscess drainage overall was 90.9 % (30/33) and was 93.3 % (27/30) in patients in whom EUS-guided drainage was attempted, with 16.5 % (n = 5) re-intervention rate. CONCLUSION: EUS-guided drainage has an excellent success rate in drainage of pelvic abscesses.

Celiac disease suspected at endoscopy in patients with chronic liver disease.

Endoscopic findings of celiac disease have high specificity and sensitivity. We evaluated records of 137 consecutive patients who had endotherapy for variceal hemorrhage, and who had features of celiac disease at endoscopy; patients who had such markers at endoscopy had undergone duodenal histology and serology. Thirty-one patients had changes of portal hypertensive vasculopathy in the duodenum, 8 had scalloping, and 6 had mosaic pattern; 3 patients also had decreased fold height or sparse folds in the descending duodenum. Six of these 8 patients had positive serology and histology suggestive of celiac disease. Endoscopic evaluation resulted in diagnosis of CD in 4.37% patients of chronic liver disease undergoing endotherapy.

Clinical profile and outcome of aluminum phosphide-induced esophageal strictures.

Aluminum phosphide (AlP) is a lethal solid fumigant pesticide which has been recently linked to esophageal stricture formation. This paper aims to study the clinical profile and response to treatment of AlP-induced esophageal strictures. Data on all patients of AlP-induced strictures seen between January 2004 and June 2008 were retrieved and analyzed for clinical parameters and response to endoscopic dilation. Each patient underwent barium swallow to define the site and length of stricture and then was dilated endoscopically. Twelve patients of AlP-induced esophageal stricture (seven males) with a mean age of 26.83+/-8.43 years were evaluated. They had consumed one to three AlP tablets, 4-156 weeks before reporting to us. They had onset of dysphagia within 2 to 8 weeks of ingestion of AlP. Of 14 strictures in 12 patients, seven were in upper third, two in middle third, and five in lower third of esophagus with a mean length of 1.96+/-0.75 cm. Nine patients responded to dilation requiring 5.56+/-2.65 dilations. Four patients were given intralesional steroids to augment the effect of dilation. Three patients failed and were operated upon. All patients remained symptom free over a follow-up of 3-30 (15.67+/-9.41) months. AlP-induced esophageal strictures can be dilated endoscopically in a majority of patients; however, 25% of them require surgical intervention. AlP-induced esophageal strictures, thus, behave like caustic-induced strictures.