Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

A knowledge-based software for the preliminary design of seismically isolated bridges

Seismic design of isolated bridges involves conceptual, preliminary and detailed structural design. However, despite the variety of commercial software currently available for the analysis and design of such systems, conceptual and preliminary design can prove to be a non-straightforward procedure because of the sensitivity of bridge response on the initial decisions made by the designer of the location, number and characteristics of the bearings placed, as well as on a series of broader criteria such as serviceability, target performance level and cost-effectiveness of the various design alternatives. Given the lack of detailed design guidelines to ensure, at this preliminary stage, compliance with the above requirements, a "trial and error" procedure is typically followed in the design office to decide on the most appropriate design scheme in the number and location of the bearing systems; the latter typically based on engineering judgment to balance performance with cost. To this end, the particular research effort aims to develop a decision-making system for the optimal preliminary design of seismically isolated bridges, assumed to respond as single degree of freedom (SDOF) systems. The proposed decision-making process is based on the current design provisions of Eurocode 8, but is complemented by additional criteria set according to expert judgment, laboratory testing and recent research findings, while using a combined cost/performance criterion to select from a database of bearings available on the international market. Software is also developed for the implementation of the system. The paper concludes with the application, and essentially the validation of the methodology and software developed through more rigorous MDOF numerical analysis for the case of a real bridge. © Springer Science+Business Media B.V. 2011

Effects on varietal aromas during wine making: a review of the impact of varietal aromas on the flavor of wine

Although there are many chemical compounds present in wines, only a few of these compounds contribute to the sensory perception of wine flavor. This review focuses on the knowledge regarding varietal aroma compounds, which are among the compounds that are the greatest contributors to the overall aroma. These aroma compounds are found in grapes in the form of nonodorant precursors that, due to the metabolic activity of yeasts during fermentation, are transformed to aromas that are of great relevance in the sensory perception of wines. Due to the multiple interactions of varietal aromas with other types of aromas and other nonodorant components of the complex wine matrix, knowledge regarding the varietal aroma composition alone cannot adequately explain the contribution of these compounds to the overall wine flavor. These interactions and the associated effects on aroma volatility are currently being investigated. This review also provides an overview of recent developments in analytical techniques for varietal aroma identification, including methods used to identify the precursor compounds of varietal aromas, which are the greatest contributors to the overall aroma after the aforementioned yeast-mediated odor release