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040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9780367823467
_q(electronic bk.)
020 _a0367823462
_q(electronic bk.)
020 _a9781000789058
_q(electronic bk. : EPUB)
020 _a1000789055
_q(electronic bk. : EPUB)
020 _a9781000788990
_q(electronic bk. : PDF)
020 _a1000788997
_q(electronic bk. : PDF)
020 _z9780367422103
020 _z0367422107
020 _z9781032305752
020 _z1032305754
020 _z0367823462
024 7 _a10.1201/9780367823467
_2doi
035 _a(OCoLC)1344340017
035 _a(OCoLC-P)1344340017
050 4 _aTA656.6
_b.L48 2022eb
072 7 _aCOM
_x004000
_2bisacsh
072 7 _aUYQ
_2bicssc
082 0 4 _a624.1/710285
_222
245 0 0 _aLeveraging artificial intelligence in engineering, management, and safety of infrastructure /
_ceditor M.Z. Naser, Assistant Professor, Clemson University, Clemson, South Carolina, USA.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2022.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 2 _aConvolutional neural networks and applications on civil infrastructure / Onur Avci, Osama Abdeljaber, Serkan Kiranyaz, Turker Ince and Daniel J. Inman -- Identifying non-linearity in construction workers' personality : safety behaviour predictive relationship using neural network and linear regression modelling / Yifan Gao, Vicente A. González, Tak Wing Yiu and Guillermo Cabrera-Guerrero -- Machine learning framework for predicting failure mode and flexural capacity of FRP-Reinforced beams / Ahmad N. Tarawneh and Eman F. Saleh.
520 _a"The design, construction, and upkeep of infrastructure comprises of a multitude of dimensions spanning a highly complex paradigm of interconnecting opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, fortunately, artificial intelligence (AI) presents novel and out-of-the-box solutions that can effectively tackle growing demands of modern and aging infrastructure including specifics regarding to structural design, traffic planning, energy requirements, human behavior etc. - especially in this era where infrastructure is reaching new heights, serving larger populations, and expected to withstand increasing natural and manmade threats. All in, this book highlights the growing inertia of utilizing AI to realize contemporary, smart and safe infrastructure. This is an emerging area that has not fully matured and is expected to draw considerable interest, attention and research in the years to come. This book marks a tangible attempt into assembling relative works, of interdisciplinary backgrounds, to a state-of-the-art handbook. In a sense, this book presents results of innovative efforts supplemented with case studies that can be used as benchmarks to carryout future experiments and/or facilitate development of advanced numerical models. Thus, this handbook aims to revolutionize the state of infrastructural engineering and sciences through fostering a new set of approaches that capitalizes on AI as their main drive. This book is written with the intention to serve as a guide for a wide audience including graduate and senior undergraduate students, professionals and government officials of civil, traffic and computer engineering backgrounds as well as for those engaged in urban planning discipline and human sciences"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aStructural health monitoring
_xData processing.
650 0 _aPublic works
_xInspection
_xData processing.
650 0 _aArtificial intelligence.
650 7 _aCOMPUTERS / Artificial Intelligence
_2bisacsh
700 1 _aNaser, M. Z.,
_eeditor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780367823467
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c4411
_d4411