國立虎尾科技大學 |

Data Analytics for Engineering and Construction Project Risk Management

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data Analytics for Engineering and Construction Project Risk Management/ by Ivan Damnjanovic, Kenneth Reinschmidt.
Author:	Damnjanovic, Ivan.
other author:	Reinschmidt, Kenneth.
Description:	XV, 379 p. 112 illus., 108 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Building—Superintendence. -
Online resource:	https://doi.org/10.1007/978-3-030-14251-3
ISBN:	9783030142513

Data Analytics for Engineering and Construction Project Risk Management
Damnjanovic, Ivan.

Data Analytics for Engineering and Construction Project Risk Management[electronic resource] /by Ivan Damnjanovic, Kenneth Reinschmidt. - 1st ed. 2020. - XV, 379 p. 112 illus., 108 illus. in color.online resource. - Risk, Systems and Decisions,2626-6717. - Risk, Systems and Decisions,.

1.Introduction to Risk and Uncertainty -- 2.Project Risk Management Framework -- 3.Project Data -- 4.Probability Theory Background -- 5.Project Planning and Estimating -- 6.Project Monitoring and Control -- 7.Case Studies and Implementation Framework.

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.

ISBN: 9783030142513

Standard No.: 10.1007/978-3-030-14251-3doiSubjects--Topical Terms:

1254752
Building—Superintendence.

LC Class. No.: TH438

Dewey Class. No.: 624

Data Analytics for Engineering and Construction Project Risk Management
LDR:03002nam a22004095i 4500 001 1018244
003 DE-He213
005 20200706073546.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030142513 $9 978-3-030-14251-3
024 7 $a 10.1007/978-3-030-14251-3 $2 doi
035 $a 978-3-030-14251-3
050 4 $a TH438
072 7 $a TNT $2 bicssc
072 7 $a TEC005000 $2 bisacsh
072 7 $a TNT $2 thema
082 0 4 $a 624 $2 23
100 1 $a Damnjanovic, Ivan. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313181
245 1 0 $a Data Analytics for Engineering and Construction Project Risk Management $h [electronic resource] / $c by Ivan Damnjanovic, Kenneth Reinschmidt.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XV, 379 p. 112 illus., 108 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Risk, Systems and Decisions, $x 2626-6717
505 0 $a 1.Introduction to Risk and Uncertainty -- 2.Project Risk Management Framework -- 3.Project Data -- 4.Probability Theory Background -- 5.Project Planning and Estimating -- 6.Project Monitoring and Control -- 7.Case Studies and Implementation Framework.
520 $a 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.
650 0 $a Building—Superintendence. $3 1254752
650 0 $a Construction industry—Management. $3 1254753
650 0 $a Construction superintendence. $3 1254754
650 0 $a Quality control. $3 573723
650 0 $a Reliability. $3 573603
650 0 $a Industrial safety. $3 568114
650 0 $a Project management. $3 558297
650 1 4 $a Construction Management. $3 671020
650 2 4 $a Quality Control, Reliability, Safety and Risk. $3 671184
650 2 4 $a Project Management. $3 787187
700 1 $a Reinschmidt, Kenneth. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313182
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030142506
776 0 8 $i Printed edition: $z 9783030142520
776 0 8 $i Printed edition: $z 9783030142537
830 0 $a Risk, Systems and Decisions, $x 2626-6717 $3 1271722
856 4 0 $u https://doi.org/10.1007/978-3-030-14251-3
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)