" "



:






Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering
: Wengang Zhang, Yanmei Zhang, Xin Gu
: Springer
: 2022
: 143
:
: pdf (true), epub
: 50.2 MB

This book summarizes the application of soft computing techniques, Machine Learning approaches, Deep Learning algorithms and optimization techniques in geoengineering including tunnelling, excavation, pipelines, etc. and geoscience including the geohazards, rock and soil properties, etc. The book features state-of-the-art studies on use of SC, ML, DL and optimizations in Geoengineering and Geoscience. Considering these points and understanding, this book will be compiled with highly focussed chapters that will discuss the application of SC, ML, DL and optimizations in Geoengineering and Geoscience.

Before introduction of more commonly used Artificial intelligence (AI), Machine learning (ML), Deep learning (DL) and Optimization algorithm (OA) technical expressions, the definition of Soft computing (SC) should be firstly mentioned since the former three terms are more relevant with each other.

SC is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just rather time-consuming to solve with current hardware. SC is sometimes referred to as computational intelligence, for comparison with the hard computing. It provides an approach to problem-solving using means other than computers. With the human mind as a role model, SC is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models. The tolerance of SC allows researchers to approach some problems that traditional computing can't process.

Generally speaking for ML, it can be considered as a method to realize AI, capable of capturing the internal patterns from data and then provide a rational decision as a guidance. Inspired by the way that human brains process information, DL is proposed as a main branch of ML, which always use more complex multi-layer neural network architectures. Compared with other ML methods, DL requires less human guidance, while requires enormous amounts of data to explore complex, various, and inherent relationships hidden in data. OA denotes the optimization algorithms. The essence of most ML algorithms is to build a surrogate model and then optimize the objective function (or loss function) through OA, to obtain the optimal model with the best performance.

Target audience: (1) Students of UG, PG, and Research Scholars: Several applications of SC, ML, DL and optimizations in Geoengineering and Geoscience can help students to enhance their knowledge in this domain. (2) Industry Personnel and Practitioner: Practitioners from different fields can be able to implement standard and advanced SC,ML,DL and optimizations for solving critical problems of civil engineering.

Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering












TURBOBIT.NET? , !





: Ingvar16 13-10-2021, 13:16 | |
 
, .





:

, , .


 MirKnig.Su  2021