Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
9,658
result(s) for
"Liquefaction"
Sort by:
Review of soil liquefaction characteristics during major earthquakes of the twenty-first century
Liquefaction, which can be defined as a loss of strength and stiffness in soils, is one of the major causes of damage to buildings and infrastructure during an earthquake. To overcome a lack of comprehensive analyses of seismically induced liquefaction, this study reviews the characteristics of liquefaction and its related damage to soils and foundations during earthquakes in the first part of the twenty-first century. Based on seismic data analysis, macroscopic phenomena of liquefaction (e.g., sand boiling, ground cracking, and lateral spread) are summarized, and several new phenomena related to earthquakes from the twenty-first century are highlighted, including liquefaction in areas with moderate seismic intensity, liquefaction of gravelly soils, liquefaction of deep-level sandy soils, re-liquefaction in aftershocks, liquid-like behavior of unsaturated sandy soils. Additionally, phenomena related to damage in soils and foundations induced by liquefaction are investigated and discussed.
Journal Article
Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing
Liquefaction prediction is an important issue in the seismic design of engineering structures, and research on this topic has been continuing in current literature using different methods, including experimental, numerical, or soft computing. In this paper, three robust machine learning (ML) algorithms are applied to predict soil liquefaction using a set of 411 shear wave velocity case records. The Genetic Algorithm (GA) based feature selection (FS) and parameter optimization of Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms are utilized to improve the accuracy of the liquefaction prediction models. Simple Random Sampling (SRS) and Stratified Random Sampling (StrRS) are used for data sampling, and also SMOTE algorithm are applied to prepare the balanced training sets. The results of robust ML algorithms are assessed based on well-known five performance matrices, namely Accuracy (Acc), Kappa, Precision, Recall, and F-Measure. Evaluation of the results is made separately for each ML algorithm considering sampling data generated from SRS, StrRS, and SMOTE. As a result, the XGBoost model is more accurate (Acc = 96%) than RF (Acc = 93%) and SVM (Acc = 91%) in the case of the SMOTE algorithm. This study reveals the superiority of the XGBoost algorithm in the liquefaction prediction and shows how the accuracy measures tend to improve when the predictive models are trained using balanced samples with StrRS and SMOTE sampling strategies.
Journal Article
Biomass Valorization of Walnut Shell for Liquefaction Efficiency
Globally, lignocellulosic biomass has great potential for industrial production of materials and products, but this resource must be used in an environmentally friendly, socially acceptable and sustainable manner. Wood and agricultural residues such as walnut shells as lignocellulosic biomass are one of the most affordable and important renewable resources in the world, which can partially replace fossil resources. The overall objective of the research is to provide background information that supports new applications of walnut shells in a biorefinery context and to increase the economic value of these non-wood forest products. This paper presents the properties characterization of liquefied biomass according to their chemical composition. All results were compared to liquefied wood. In this study, the liquefaction properties of five different walnut shell particle sizes were determined using glycerol as the liquefaction reagent under defined reaction conditions. The liquefied biomass was characterized for properties such as percentage residue, degree of liquefaction, and hydroxyl OH numbers. The chemical composition of the same biomass was investigated for its influence on the liquefaction properties. Accordingly, the main objective of this study was to determine the liquefaction properties of different particle sizes as a function of their chemical composition, also in comparison with the chemical composition of wood. The study revealed that walnut shell biomass can be effectively liquefied into glycerol using H2SO4 as the catalyst, with liquefaction efficiency ranging from 89.21 to 90.98%.
Journal Article
Cleaving arene rings for acyclic alkenylnitrile synthesis
Synthetic chemistry is built around the formation of carbon–carbon bonds. However, the development of methods for selective carbon–carbon bond cleavage is a largely unmet challenge
1
–
6
. Such methods will have promising applications in synthesis, coal liquefaction, petroleum cracking, polymer degradation and biomass conversion. For example, aromatic rings are ubiquitous skeletal features in inert chemical feedstocks, but are inert to many reaction conditions owing to their aromaticity and low polarity. Over the past century, only a few methods under harsh conditions have achieved direct arene-ring modifications involving the cleavage of inert aromatic carbon–carbon bonds
7
,
8
, and arene-ring-cleavage reactions using stoichiometric transition-metal complexes or enzymes in bacteria are still limited
9
–
11
. Here we report a copper-catalysed selective arene-ring-opening reaction strategy. Our aerobic oxidative copper catalyst converts anilines, arylboronic acids, aryl azides, aryl halides, aryl triflates, aryl trimethylsiloxanes, aryl hydroxamic acids and aryl diazonium salts into alkenyl nitriles through selective carbon–carbon bond cleavage of arene rings. This chemistry was applied to the modification of polycyclic aromatics and the preparation of industrially important hexamethylenediamine and adipic acid derivatives. Several examples of the late-stage modification of complex molecules and fused ring compounds further support the potential broad utility of this methodology.
Common aromatic rings, such as anilines, arylboronic acids and aryl halides, can be opened up and converted to alkenyl nitriles through carbon–carbon bond cleavage using a copper catalyst.
Journal Article
FTIR Monitoring of Polyurethane Foams Derived from Acid-Liquefied and Base-Liquefied Polyols
Polyalcohol liquefaction can be performed by acid or base catalysis, producing polyols with different properties. This study compared the mechanical properties of foams produced using polyols from liquefied Cytisus scoparius obtained by acid and base catalysis and using two different foam catalysts. The differences were monitored using FTIR analysis. Acid-catalyzed liquefaction yielded 95.1%, with the resultant polyol having an OH index of 1081 mg KOH/g, while base catalysis yielded 82.5%, with a similar OH index of 1070 mg KOH/g. Generally, compressive strength with dibutyltin dilaurate (DBTDL) ranged from 16 to 31 kPa (acid-liquefied polyol) and 12 to 21 kPa (base-liquefied polyol), while with stannous octoate (TIN), it ranged from 17 to 42 kPa (acid) and 29 to 68 kPa (base). Increasing water content generally decreased the compressive modulus and strength of the foams. Higher water content led to a higher absorption at 1670 cm−1 in the FTIR spectrum due to the formation of urea. Higher isocyanate indices generally improved compressive strength, but high amounts led to unreacted isocyanate that could be seen by a higher absorption at 2265 cm−1 and 3290 cm−1. DBTL was shown to be the best foam catalyst due to higher trimer conversion seen in the spectra by a higher absorption at 1410 cm−1. Acid- and base-derived polyols lead to different polyurethane foams with different FTIR spectra, particularly with a higher absorption at 1670 cm−1 for foams from acid-derived liquefaction.
Journal Article
Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches
This study investigates the effectiveness of various deep learning (DL) algorithms in predicting soil liquefaction susceptibility. We explore a spectrum of algorithms, including machine learning models such as Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Logistic Regression (LR), alongside DL architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Bidirectional LSTMs (BiLSTMs), and Gated Recurrent Units (GRUs). The performance of these algorithms is assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve analysis, and area under the curve (AUC). Cross-entropy loss is employed as the loss function during model training to optimize the differentiation between liquefiable and non-liquefiable soil samples. Our findings reveal that the GRU model achieved the highest overall accuracy of 0.98, followed by the BiLSTM model with an accuracy of 0.95. Notably, the BiLSTM model excelled in precision for class 1, attaining a score of 0.96 on the test dataset. These results underscore the potential of both GRU and BiLSTM models in predicting soil liquefaction susceptibility, with the BiLSTM model’s simpler architecture proving particularly effective in certain metrics and datasets. The findings of this study could assist practitioners in seismic risk assessment by providing more accurate and reliable tools for evaluating soil liquefaction potential, thereby enhancing mitigation strategies and informing decision-making in earthquake-prone areas. This study contributes to developing robust tools for liquefaction hazard assessment, ultimately supporting improved seismic risk mitigation.
Journal Article