The.Hottest

Monday, September 30, 2024 , ,
Jianye Ching ... 188 pages - Language: ‎English - Publisher: CRC Press; (August, 2024).

Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.

Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.
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Saturday, September 21, 2024 , ,
Wengang Zhang, Yanmei Zhang, Runhong Zhang, Yongqin Li, Li Hong, Yuntao Yuan ... 770 pages - Language: English - Publisher: Elsevier; (May, 2024).

Design of Deep Braced Excavation in Urban Geotechnical Environments presents the design and calculation of deep braced excavation in urban geotechnical environments considering the soil characteristics of spatial variability and anisotropy, as well as excavation responses of urban adjacent utilities such as pipelines, tunnels, foundation piles and buildings. With economic development and further urbanization, excavation goes deeper, longer, and larger in scale. These conditions require advanced analysis and design methods.

The text begins with coverage of overall stability, earth pressure and strut force, retaining wall responses, and ground movements. Numerical modeling and computer codes are outlined in detail. The authors then take a close look at the design of jet grouting for deformation control, instrumentation and back analysis for excavation, and the characteristics of deep braced excavation in complex geological environment. The final chapters conclude with an examination of the determination of excavation responses of urban adjacent utilities and failure analysis based on several case histories.

This book provides the latest knowledge in both theory and practical application for geotechnical design engineers working on deep excavation problems. Presents the latest theory and knowledge surrounding deep braced excavations + Systematically outlines theory, field reports, calculations, and back analysis, making it much easier to master the work relevant with deep braced excavations + Provides detailed visual elements and hyperlinks to additional images and video for case excavations to enhance understanding.
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Sunday, September 15, 2024 , ,
Brij B. Gupta, Mamta ... 369 pages - Language: ‎English - Publisher: ‎World Scientific Publishing Company; (December, 2023).

With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.

Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.

Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.

Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.
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