LEADER 01688nam 2200409Ia 450 001 996385878903316 005 20221108004548.0 035 $a(CKB)4940000000075982 035 $a(EEBO)2240962949 035 $a(OCoLC)12258389 035 $a(EXLCZ)994940000000075982 100 $a19850712d1670 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 14$aThe several statutes concerning bankrupts methodically digested$b[electronic resource] $etogether with the resolutions of our learned judges on them : as likewise the statutes 13th Eliz. and 27 Eliz. touching fraudulent conveyances, with the like resolutions on them /$fby T.B., Esq 210 $aLondon $cPrinted for T. Twyford$d1670 215 $a[2], 206 p 300 $aAttributed to T. Blount. Cf. BM. 300 $aTitle on added t.p.: The resolutions of the judges upon the several statutes of bankrupts, as also the like resolutions upon 13 Eliz. and 27 Eliz. touching fraudulent conveyances. 300 $aIncludes index. 300 $aReproduction of original in British Library. 330 $aeebo-0018 606 $aBankruptcy$zEngland$xLaw and legislation$vEarly works to 1800 606 $aFraudulent conveyances$zEngland$xLaw and legislation$vEarly works to 1800 615 0$aBankruptcy$xLaw and legislation 615 0$aFraudulent conveyances$xLaw and legislation 700 $aBlount$b Thomas$f1618-1679.$0838907 801 0$bEAA 801 1$bEAA 801 2$bm/c 801 2$bEAA 801 2$bUMI 801 2$bWaOLN 906 $aBOOK 912 $a996385878903316 996 $aThe several statutes concerning bankrupts methodically digested$92342134 997 $aUNISA LEADER 05180nam 22005295 450 001 9910878059703321 005 20250723002353.0 010 $a9783031660511$b(electronic bk.) 010 $z9783031660504 024 7 $a10.1007/978-3-031-66051-1 035 $a(MiAaPQ)EBC31572261 035 $a(Au-PeEL)EBL31572261 035 $a(CKB)33566328000041 035 $a(DE-He213)978-3-031-66051-1 035 $a(OCoLC)1451520869 035 $a(EXLCZ)9933566328000041 100 $a20240729d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplications of Artificial Neural Networks and Machine Learning in Civil Engineering /$fby Ali Kaveh 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (483 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1168 311 08$aPrint version: Kaveh, Ali Applications of Artificial Neural Networks and Machine Learning in Civil Engineering Cham : Springer International Publishing AG,c2024 9783031660504 327 $aArtificial Intelligence Background, Applications and Future -- Buckling Resistance Prediction of High Strength Steel Columns Using Metaheuristic Trained Artificial Neural Networks -- The Use of Artificial Neural Networks and Metaheuristic Algorithms to Optimize the Compressive Strength of Concrete -- Design of Double Layer Grids Using Backpropagation Neural Networks -- Analysis of Double Layer Barrel Vaults Using Different Neural Networks -- BP and RBF Neural Networks for Predicting Displacements and Design of Schwedler dome -- Structural Optimization by Gradient Based Neural Networks -- Comparative Study of Backpropagation and Improved Counter propagation Neural Nets in Structural Analysis and Optimization -- Hybrid ECBO ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls -- Shape Optimization of Arch Dams with Frequency Constraints by Enhanced Charged System Search Algorithm and Neural Network -- Estimation of the Vulnerability of the Concrete Structures Using Artificial Neural Networks -- Efficient Training of Artificial Neural Networks Using Different Meta heuristic Algorithms for Predicting the FRP Strength -- A Metaheuristic Based Artificial Neural Network for Plastic Limit Analysis of Frames -- Wavefront Reduction Using Graphs, Neural Networks and Genetic Algorithm -- Optimal Design of Transmission Towers Using Genetic Algorithm and Neural Networks -- Stimating the Vulnerability of the Concrete Moment Resisting Frame Structures Using Artificial Neural Networks -- A Hybrid Graph Neural Method for Domain Decomposition -- GMDH based Prediction of Shear Strength of FRP RC Beams With and Without Stirrups -- Efficient Training of Two ANNs Using Four Meta-heuristic Algorithms for Predicting the FRP Strength -- New Predictive Models for Prediction of Bond Strength Between FRP Reinforcements Externally Glued on Masonry Units -- Kernel Extreme Learning Machine Application in Prediction of Bond Strength Between EBR FRP and Concrete Substrate -- Development of Predictive Models for Shear Strength of HSC Slender Beams Without Web Reinforcement Using Machine Learning Based Techniques. 330 $aThis book provides different applications of artificial neural networks (ANN) and machine learning (ML) in various problems of material science, structural optimization, and optimal analysis of structures in twenty two chapters. Nowadays, the world has witnessed unprecedented advances in technology and computer science. Artificial intelligence has emerged as a top field captivating global attention. Often referred to as AI, this technology stands apart from other disciplines as it aims to design machines and systems that exhibit intelligence, learn autonomously, and make decisions akin to humans. In order to comprehend the impact of this innovation, one must delve into the workings of artificial intelligence, trace its historical evolution from inception to the present day, and explore its diverse applications in domains like medicine, transportation, broadcasting, and marketing. Artificial intelligence introduces a transformative element to our reality, fostering significant breakthroughs and innovations. The book is used in any AI course, in particular, in Civil Engineering. It is also utilized in various fields of Industrial Civil Engineering. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1168 606 $aComputational intelligence 606 $aCivil engineering 606 $aComputational Intelligence 606 $aCivil Engineering 615 0$aComputational intelligence. 615 0$aCivil engineering. 615 14$aComputational Intelligence. 615 24$aCivil Engineering. 676 $a006.3 700 $aKaveh$b A$g(Ali),$f1948-$01596023 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910878059703321 996 $aApplications of Artificial Neural Networks and Machine Learning in Civil Engineering$94408690 997 $aUNINA