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Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this purpose, a Genetic Algorithm (GA) was developed to select the most significant features in the raw dataset. After that, a GA-DLNN hybrid model was developed to select optimal parameters for the DLNN model, including: network algorithm, activation function for hidden neurons, number of hidden layers, and the number of neurons in each hidden layer. A database containing 472 driven pile static load test reports was used. The dataset was divided into three parts, namely the training set (60%), validation (20%) and testing set (20%) for the construction, validation and testing phases of the proposed model, respectively. Various quality assessment criteria, namely the coefficient of determination (R 2 ), Index of Agreement (IA), mean absolute error (MAE) and root mean squared error (RMSE), were used to evaluate the performance of the machine learning (ML) algorithms. The GA-DLNN hybrid model was shown to exhibit the ability to find the most optimal set of parameters for the prediction process.The results showed that the performance of the hybrid model using only the most critical features gave the highest accuracy, compared with those obtained by the hybrid model using all input variables.

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

Accurate prediction of pile bearing capacity is an important part of foundation engineering. Notably, the determination of pile bearing capacity through an in situ load test is costly and time-consuming. Therefore, this study focused on developing a machine learning algorithm, namely, Ensemble Learning (EL), using weight voting protocol of three base machine learning algorithms, gradient boosting (GB), random forest (RF), and classic linear regression (LR), to predict the bearing capacity of the pile. Data includes 108 pile load tests under different conditions used for model training and testing. Performance evaluation indicators such as R-square (R2), root mean square error (RMSE), and MAE (mean absolute error) were used to evaluate the performance of models showing the efficiency of predicting pile bearing capacity with outstanding performance compared to other models. The results also showed that the EL model with a weight combination of w1 = 0.482, w2 = 0.338, and w3 = 0.18 corresponding to the models GB, RF, and LR gave the best performance and achieved the best balance on all data sets. In addition, the global sensitivity analysis technique was used to detect the most important input features in determining the bearing capacity of the pile. This study provides an effective tool to predict pile load capacity with expert performance.

Abstract Background Antimicrobial stewardship (AMS) programmes have been implemented around the world to guide rational use of antibiotics but implementation is challenging, particularly in low- and middle-income countries, including Vietnam. Understanding factors influencing doctors’ prescribing choices for empirical treatment can help design AMS interventions in these settings. Objectives To understand doctors’ choices of antibiotics for empirical treatment of common bacterial infections and the factors influencing decision-making. Methods We conducted a cross-sectional survey among medical professionals applying for a postgraduate programme at Hanoi Medical University, Vietnam. We used a published survey developed for internal medicine doctors in Canada. The survey was self-administered and included four clinical scenarios: (i) severe undifferentiated sepsis; (ii) mild undifferentiated sepsis; (iii) severe genitourinary infection; and (iv) mild genitourinary infection. Results A total of 1011/1280 (79%), 683/1188 (57.5%), 718/1157 (62.1%) and 542/1062 (51.0%) of the participants selected combination therapy for empirical treatment in scenarios 1, 2, 3 and 4, respectively. Undifferentiated sepsis (OR 1.82, 95% CI 1.46–2.27 and 2.18, 1.51–3.16 compared with genitourinary) and severe infection (1.33, 1.24–1.43 and 1.38, 1.21–1.58 compared with mild) increased the likelihood of choosing a combination therapy and a carbapenem regimen, respectively. Participants with higher acceptable minimum threshold for treatment coverage and young age were also more likely to prescribe carbapenems. Conclusions Decision-making in antibiotic prescribing among doctors in Vietnam is influenced by both disease-related characteristics and individual factors, including acceptable minimum treatment coverage. These findings are useful for tailoring AMS implementation in Vietnam and other, similar settings.

Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

This study focuses on the use of deep neural network (DNN) to predict the soil friction angle, one of the crucial parameters in geotechnical design. Besides, particle swarm optimization (PSO) algorithm was used to improve the performance of DNN by selecting the best structural DNN parameters, namely, the optimal numbers of hidden layers and neurons in each hidden layer. For this aim, a database containing 245 laboratory tests collected from a project in Ho Chi Minh city, Vietnam, was used for the development of the proposed hybrid PSO-DNN model, including seven input factors (soil state, standard penetration test value, unit weight of soil, void ratio, thickness of soil layer, top elevation of soil layer, and bottom elevation of soil layer) and the friction angle was considered as the target. The data set was divided into three parts, namely, the training, validation, and testing sets for the construction, validation, and testing phases of the model. Various quality assessment criteria, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used to estimate the performance of PSO-DNN models. The PSO algorithm showed a remarkable ability to find out an optimal DNN architecture for the prediction process. The results showed that the PSO-DNN model using 10 hidden layers outperformed the DNN model, in which the average correlation improvement increased R2 by 1.83%, MAE by 5.94%, and RMSE by 8.58%. Besides, a global sensitivity analysis technique was used to detect the most important inputs, and it showed that, among the seven input variables, the elevation of top and bottom of soil played an important role in predicting the friction angle of soil.

Community acquired K. pneumoniae pneumonia is still common in Asia and is reportedly associated with alcohol use. Oropharyngeal carriage of K. pneumoniae could potentially play a role in the pathogenesis of K. pneumoniae pneumonia. However, little is known regarding K. pneumoniae oropharyngeal carriage rates and risk factors. This population-based cross-sectional study explores the association of a variety of demographic and socioeconomic factors, as well as alcohol consumption with oropharyngeal carriage of K. pneumoniae in Vietnam. 1029 subjects were selected randomly from age, sex, and urban and rural strata. An additional 613 adult men from a rural environment were recruited and analyzed separately to determine the effects of alcohol consumption. Demographic, socioeconomic, and oropharyngeal carriage data was acquired for each subject. The overall carriage rate of K. pneumoniae was 14.1% (145/1029, 95% CI 12.0%-16.2%). By stepwise logistic regression, K. pneumoniae carriage was found to be independently associated with age (OR 1.03, 95% CI 1.02-1.04), smoking (OR 1.9, 95% CI 1.3-2.9), rural living location (OR 1.6, 95% CI 1.1-2.4), and level of weekly alcohol consumption (OR 1.7, 95% CI 1.04-2.8). Moderate to heavy weekly alcohol consumption, old age, smoking, and living in a rural location are all found to be associated with an increased risk of K. pneumoniae carriage in Vietnamese communities. Whether K. pneumoniae carriage is a risk factor for pneumonia needs to be elucidated.

The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.

Background To formulate effective strategies for antimicrobial stewardship (AMS) in primary care, it is crucial to gain a thorough understanding of factors influencing prescribers' behavior within the context. This qualitative study utilizes the Theoretical Domains Framework (TDF) to uncover these influential factors. Methods We conducted a qualitative study using in-depth interviews and focus group discussions with primary care workers in two provinces in rural Vietnam. Data analysis employed a combined inductive and deductive approach, with the deductive aspect grounded in the TDF. Results Thirty-eight doctors, doctor associates, and pharmacists participated in twenty-two interviews and two focus group discussions. We identified sixteen themes, directly mapping onto seven TDF domains: knowledge, skills, behavioral regulation, environmental context and resources, social influences, social/professional role and identity, and optimism. Factors driving unnecessary prescription of antibiotics include low awareness of antimicrobial resistance (AMR), diagnostic uncertainty, prescription-based reimbursement policy, inadequate medication supplies, insufficient financing, patients’ perception of health insurance medication as an entitlement, and maintaining doctor-patient relationships. Potential factors facilitating AMS activities include time availability for in-person patient consultation, experience in health communication, and willingness to take action against AMR. Conclusion Utilizing the TDF to systematically analyze and present behavioral determinants offers a structured foundation for designing impactful AMS interventions in primary care. The findings underscore the importance of not only enhancing knowledge and skills but also implementing environmental restructuring, regulation, and enablement measures to effectively tackle unnecessary antibiotic prescribing in this context.