Data Science For Civil Engineering

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Data Science for Civil Engineering

Rakesh K. Jain,Prashant Shantaram Dhotre,Deepak Tatyasaheb Mane,Parikshit Narendra Mahalle
Data Science for Civil Engineering Book PDF

Author : Rakesh K. Jain,Prashant Shantaram Dhotre,Deepak Tatyasaheb Mane,Parikshit Narendra Mahalle
Publisher : CRC Press
Page : 251 pages
File Size : 49,7 Mb
Release : 2023-05-10
Category : Computers
ISBN : 9781000873467

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Data Science for Civil Engineering by Rakesh K. Jain,Prashant Shantaram Dhotre,Deepak Tatyasaheb Mane,Parikshit Narendra Mahalle Pdf

This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.

Probabilistic Machine Learning for Civil Engineers

James-A. Goulet
Probabilistic Machine Learning for Civil Engineers Book PDF

Author : James-A. Goulet
Publisher : MIT Press
Page : 298 pages
File Size : 43,8 Mb
Release : 2020-03-16
Category : Computers
ISBN : 9780262358019

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Probabilistic Machine Learning for Civil Engineers by James-A. Goulet Pdf

An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.

Data Analytics for Engineering and Construction Project Risk Management

Ivan Damnjanovic,Kenneth Reinschmidt
Data Analytics for Engineering and Construction Project Risk Management Book PDF

Author : Ivan Damnjanovic,Kenneth Reinschmidt
Publisher : Springer
Page : 379 pages
File Size : 47,6 Mb
Release : 2019-05-23
Category : Technology & Engineering
ISBN : 9783030142513

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Data Analytics for Engineering and Construction Project Risk Management by Ivan Damnjanovic,Kenneth Reinschmidt Pdf

This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.

A Primer on Machine Learning Applications in Civil Engineering

Paresh Chandra Deka
A Primer on Machine Learning Applications in Civil Engineering Book PDF

Author : Paresh Chandra Deka
Publisher : CRC Press
Page : 201 pages
File Size : 52,8 Mb
Release : 2019-10-28
Category : Computers
ISBN : 9780429836657

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A Primer on Machine Learning Applications in Civil Engineering by Paresh Chandra Deka Pdf

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises

Computing in Civil Engineering

Ioannis Brilakis,SangHyun Lee,Burçin Becerik-Gerber
Computing in Civil Engineering Book PDF

Author : Ioannis Brilakis,SangHyun Lee,Burçin Becerik-Gerber
Publisher : ASCE Publications
Page : 904 pages
File Size : 49,9 Mb
Release : 2013
Category : Technology & Engineering
ISBN : 0784413029

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Computing in Civil Engineering by Ioannis Brilakis,SangHyun Lee,Burçin Becerik-Gerber Pdf

Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering.

Foundations of Data Science for Engineering Problem Solving

Parikshit Narendra Mahalle,Gitanjali Rahul Shinde,Priya Dudhale Pise,Jyoti Yogesh Deshmukh
Foundations of Data Science for Engineering Problem Solving Book PDF

Author : Parikshit Narendra Mahalle,Gitanjali Rahul Shinde,Priya Dudhale Pise,Jyoti Yogesh Deshmukh
Publisher : Springer Nature
Page : 125 pages
File Size : 40,7 Mb
Release : 2021-08-21
Category : Technology & Engineering
ISBN : 9789811651601

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Foundations of Data Science for Engineering Problem Solving by Parikshit Narendra Mahalle,Gitanjali Rahul Shinde,Priya Dudhale Pise,Jyoti Yogesh Deshmukh Pdf

This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.

Structural Health Monitoring Based on Data Science Techniques

Alexandre Cury,Diogo Ribeiro,Filippo Ubertini,Michael D. Todd
Structural Health Monitoring Based on Data Science Techniques Book PDF

Author : Alexandre Cury,Diogo Ribeiro,Filippo Ubertini,Michael D. Todd
Publisher : Springer Nature
Page : 490 pages
File Size : 48,8 Mb
Release : 2021-10-23
Category : Computers
ISBN : 9783030817169

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Structural Health Monitoring Based on Data Science Techniques by Alexandre Cury,Diogo Ribeiro,Filippo Ubertini,Michael D. Todd Pdf

The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

Innovation in Construction

Seyed Hamidreza Ghaffar,Paul Mullett,Eujin Pei,John Roberts
Innovation in Construction Book PDF

Author : Seyed Hamidreza Ghaffar,Paul Mullett,Eujin Pei,John Roberts
Publisher : Springer Nature
Page : 466 pages
File Size : 48,9 Mb
Release : 2022-03-23
Category : Technology & Engineering
ISBN : 9783030957988

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Innovation in Construction by Seyed Hamidreza Ghaffar,Paul Mullett,Eujin Pei,John Roberts Pdf

This book tackles the complex topic of implementing innovation and the successful application of advanced technology in the construction industry. It provides a practical guide for the transformation of the industry by detailing appropriate and effective implementation methods, required skill sets and structural changes necessary to facilitate the practical and innovative application of technology. The construction industry is behind other industries in its level of innovation and adoption of technology, and is of critical importance to many of today’s global challenges, such as climate change, global warming and resource scarcity. There is therefore a need for smarter and more efficient ways of managing available resources. This book elaborates on how the innovative application of technology could offer hope for the construction industry in it’s imperative to rise to current and future global challenges. It includes the real-world case studies of innovative projects that go beyond the current state-of-the-art academic research, and have improved productivity, quality and performance in the construction sector. This book provides readers from both industrial and academic backgrounds with a comprehensive guide on transforming the construction industry with the efficient and effective implementation of technologies and modern methods of construction.

Big Data Science and Analytics for Smart Sustainable Urbanism

Simon Elias Bibri
Big Data Science and Analytics for Smart Sustainable Urbanism Book PDF

Author : Simon Elias Bibri
Publisher : Springer
Page : 337 pages
File Size : 41,5 Mb
Release : 2019-05-30
Category : Political Science
ISBN : 9783030173128

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Big Data Science and Analytics for Smart Sustainable Urbanism by Simon Elias Bibri Pdf

We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Principles of Strategic Data Science

Dr Peter Prevos
Principles of Strategic Data Science Book PDF

Author : Dr Peter Prevos
Publisher : Packt Publishing Ltd
Page : 104 pages
File Size : 43,9 Mb
Release : 2019-06-03
Category : Computers
ISBN : 9781838985509

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Principles of Strategic Data Science by Dr Peter Prevos Pdf

Take the strategic and systematic approach to analyze data to solve business problems Key FeaturesGain detailed information about the theory of data scienceAugment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use dataBook Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you’ll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data. What you will learnGet familiar with the five most important steps of data scienceUse the Conway diagram to visualize the technical skills of the data science teamUnderstand the limitations of data science from a mathematical and ethical perspectiveGet a quick overview of machine learningGain insight into the purpose of using data science in your workUnderstand the role of data science managers and their expectationsWho this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

A Hands-On Introduction to Data Science

Chirag Shah
A Hands On Introduction to Data Science Book PDF

Author : Chirag Shah
Publisher : Cambridge University Press
Page : 459 pages
File Size : 46,9 Mb
Release : 2020-04-02
Category : Business & Economics
ISBN : 9781108472449

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A Hands-On Introduction to Data Science by Chirag Shah Pdf

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Data Science in Engineering, Volume 9

Ramin Madarshahian,Francois Hemez
Data Science in Engineering Volume 9 Book PDF

Author : Ramin Madarshahian,Francois Hemez
Publisher : Springer Nature
Page : 287 pages
File Size : 55,9 Mb
Release : 2021-10-04
Category : Technology & Engineering
ISBN : 9783030760045

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Data Science in Engineering, Volume 9 by Ramin Madarshahian,Francois Hemez Pdf

Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Data Science in Engineering Applications Engineering Mathematics Computational Methods in Engineering

Supervised and Unsupervised Learning for Data Science

Michael W. Berry,Azlinah Mohamed,Bee Wah Yap
Supervised and Unsupervised Learning for Data Science Book PDF

Author : Michael W. Berry,Azlinah Mohamed,Bee Wah Yap
Publisher : Springer Nature
Page : 191 pages
File Size : 44,8 Mb
Release : 2019-09-04
Category : Technology & Engineering
ISBN : 9783030224752

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Supervised and Unsupervised Learning for Data Science by Michael W. Berry,Azlinah Mohamed,Bee Wah Yap Pdf

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Proceedings of International Conference on Data Science and Applications

Mukesh Saraswat,Chandreyee Chowdhury,Chintan Kumar Mandal,Amir H. Gandomi
Proceedings of International Conference on Data Science and Applications Book PDF

Author : Mukesh Saraswat,Chandreyee Chowdhury,Chintan Kumar Mandal,Amir H. Gandomi
Publisher : Springer Nature
Page : 908 pages
File Size : 50,9 Mb
Release : 2023-02-06
Category : Technology & Engineering
ISBN : 9789811966347

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Proceedings of International Conference on Data Science and Applications by Mukesh Saraswat,Chandreyee Chowdhury,Chintan Kumar Mandal,Amir H. Gandomi Pdf

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

Mastering Data Science

Cybellium Ltd
Mastering Data Science Book PDF

Author : Cybellium Ltd
Publisher : Cybellium Ltd
Page : 224 pages
File Size : 48,8 Mb
Release : 2024-07-01
Category : Computers
ISBN : 9798862563160

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Mastering Data Science by Cybellium Ltd Pdf

Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? "Mastering Data Science" is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: "Mastering Data Science" is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com