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Abstract In many engineering projects, the soil compression coefficient is an important parameter used for estimating the settlement of soil layers. The common practice of determining the soil compression coefficient via the oedometer test is time-consuming and expensive. This study proposes a machine learning solution to replace the conventional tests used for obtaining the coefficient of soil compression. The new approach is an integration of the Multi-Layer Perceptron Neural Network (MLP Neural Nets) and Particle Swarm Optimization (PSO). These two computational intelligence methods work synergistically to establish a prediction model of soil compression coefficient. The PSO metaheuristic is employed to optimize the MLP Neural Nets model structure. To train and validate the proposed method, named as PSO-MLP Neural Nets, a dataset of 154 soil samples featuring 12 influencing factors has been collected from the geotechnical investigation process of a high-rise building project. Experimental results show that the proposed PSO-MLP Neural Nets has attained the most accurate prediction of the soil compression coefficient performance with RMSE = 0.0267, MAE = 0.0145, and R2 = 0.884. The result of the proposed model is significantly better than those obtained from other benchmark methods including the backpropagation neural network, the radial basis function neural network, the support vector regression, the random forest, and the Gaussian process. Based on the experimental results, the newly constructed PSO-MLP Neural Nets is very potential to be a new alternative to assist geotechnical engineers in design phase of civil engineering projects. [ABSTRACT FROM AUTHOR]

• DNN was verified and assessed for shallow landslide susceptibility mapping. • Three optimization algorithms, Adam, SGD, and RMSProp, were used. • Two loss functions, MSE and CE, were investigated. • DNN with Adam and MSE is the best and outperformed other models. • DNN-Adam-MSE is a new tool for shallow landslide susceptibility modeling. This research aims at investigating the capability of Keras's deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale. Shallow landslides at the Ha Long area (Vietnam) were selected as a case study. For this regard, set of ten influencing factors (slope, aspect, curvature, topographic wetness index, landuse, distance to road, distance to river, soil type, distance to fault, and lithology) and 193 landslide polygons were prepared to construct a Geographic Information System (GIS) database for the study area. Using the collected database, the DNN with its potential of realizing complex functional mapping hidden in the data is used to generalize a decision boundary that separates the learning space into two distinct categories: landslide (a positive class) and non-landslide (a negative class). Experimental results point out that the utilized the Keras's deep learning model with the Adam optimization and the mean squared error lost function is the best with the prediction performance of 84.0%. The performance is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree. We conclude that the Keras's deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas. [ABSTRACT FROM AUTHOR]

Highlights • Assessment of detectors' locations and number of detectors in PGSFR. • Generation of spatial weighting function using forward fixed-source calculation in MCNPX. • Calculation of transient power during withdrawal of a single control assembly by using FREK. Abstract This paper presents the assessment of the ex-core detectors in the Prototype Gen-IV Sodium-cooled Fast Reactor (PGSFR) in response to the movement of single primary control assembly, to evaluate the detectors' locations and to determine a proper number of detectors. The detectors are located below the containment vessel and distributed in different ranges from the center and different azimuthal angles. Another position considered is in the radial direction of the core, at the sodium coolant gap between the outermost assembly and core shroud. To calculate the detection rate, the contribution of each fuel region to the detector and the core power distribution are required. Initially, the aforementioned contribution is generated by performing a continuous-energy forward fixed-source Monte Carlo calculation using MCNPX. The reactor kinetics code FREK simulates the power change due to an inadvertent withdrawal of a primary control assembly up to 10% from its critical position followed by full insertion of secondary control assemblies. The response from each detector location was evaluated, and consequently, the detectors located below the containment vessel, which can "see" all the fuel assemblies, responded better to the core power increase with a minor error of less than ∼0.6%. In addition, the impact of locating the detector below the containment vessel at different locations from the center or at different azimuthal angles was insignificant but several detectors should be distributed at even azimuthal angles for effective core monitoring and redundancy purposes. [ABSTRACT FROM AUTHOR]

• Sensitivity/Uncertainty analysis using SERPENT 2 and ENDF/B-VIII.0. • Error of k eff of CERCER core was significant and larger than that of CERMET one. • ENDF/B-VIII.0 was found to be insensitive to kinetic parameter simulation results. The reliability of eigenvalue and kinetic parameter calculation results for CERCER and CERMET fueled ADS was investigated by conducting sensitivity and uncertainty analysis by SERPENT 2 and ENDF/B-VIII.0 library. The total error of k eff of CERCER core was found to be 1268.7 pcm and larger than that of CERMET one (609.4 pcm). It mostly comes from cross section uncertainty of 237Np (n,g) – the biggest contributor, 239Pu (n,f), 232Th (n,g) and 237Np (n,f). Therefore, the accuracy of nuclear data of those isotopes is recommended to be improved to achieve more reliable eigenvalue results. For kinetic parameters, the uncertainty of evaluated results mainly originated from the 237Np (n,g), 239Pu (n,inl) and 23Na (n,ela) cross sections. Nevertheless, its impact on simulation accuracy is not significant (less than 1%). Thus, ENDF/B-VIII.0 nuclear data is sufficient to evaluate β eff , l eff, and λ eff and discuss the safety features of ADS. [ABSTRACT FROM AUTHOR]

The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcomes were cross-checked. The locations of existing landslides were detected by general field surveys and relevant records. 220 previous landslide locations were obtained, presented as an inventory map, and divided into four folds to calibrate and authenticate the models. For modelling the landslide susceptibility, twelve LCFs (landslide conditioning factors) were used. Two statistical methods, i.e. the mean absolute error (MAE) and the root mean square error (RMSE), one statistical test, i.e. the Freidman rank test, as well as the receiver operating characteristic (ROC), efficiency and precision were used for authenticating the produced landslide models. The results of the accuracy measures revealed that all models have good potential to recognize the landslide susceptibility in the Garhwal Himalayan region. Among these models, the ensemble model achieved a higher accuracy (precision: 0.829, efficiency: 0.833, AUC: 89.460, RMSE: 0.069 and MAE: 0.141) than the individual models. According to the outcome of the ensemble simulations, the BRT model's predictive accuracy was enhanced by integrating it with the statistical model (CP). The study showed that the areas of fallow land, plantation fields, and roadsides with elevations of more than 1500 m. with steep slopes of 24° to 87° and eroding hills are highly susceptible to landslides. The findings of this work could help in minimizing the landslides' risk in the Western Himalaya and its adjoining areas with similar landscapes and geological characteristics. Unlabelled Image • Considering the twelve landslide conditioning factors landslide susceptibility maps were prepared using CP, BRT and CP-BRT models. • After integration of BRT model with CP model the level of accuracy was increased. • Nearly 20% of the study areas have very high probability of landslide. [ABSTRACT FROM AUTHOR]