04385nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001300172072001600185072002300201072001500224245022200239250001800461264007200479300006700551336002600618337002600644338003600670347002400706490005800730505150500788520062602293650004002919650003102959650002302990650004603013650003803059650003703097650003603134700009303170700009103263700008503354700008703439710003403526773002603560776003603586776003603622776003603658830005803694856004603752912001403798912001403812999001703826978-981-97-0072-1DE-He21320251030111745.0cr nn 008mamaa240325s2024    si |    s    |||| 0|eng d  a97898197007219978-981-97-0072-17 a10.1007/978-981-97-0072-12doi 4aTH1-9745 7aTNK2bicssc 7aTEC0050502bisacsh 7aTNK2thema10aRecent Advances in Civil Engineering for Sustainable Communitiesh[electronic resource] :bSelect Proceeding of IACESD 2023 /cedited by N. Vinod Chandra Menon, Sreevalsa Kolathayar, Hugo Rodrigues, K. S. Sreekeshava.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXIV, 547 p. 282 illus., 237 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Civil Engineering,x2366-2565 ;v4590 aRecent Advances in Civil Engineering for Sustainable Communities - An Introduction -- Investigation on Compressive Strength of Fibre Reinforced Concrete Using Artificial Neural Network -- Conventional and Ensemble Machine Learning Techniques to Predict the Compressive Strength of Sustainable Concrete -- Experimental Study on Strength Properties of Concrete Incorporated with Bacteria -- Predicting the Porosity of SCM-blended Concrete Composites Using Ensemble Machine Learning Models -- Ensemble Machine Learning Models to Predict the Compressive Strength and Ultrasonic Pulse Velocity of Sustainable Concrete -- Prognosis of Concrete Strength: The State of Art in Using Different Machine Learning Algorithms -- Experimental Study on Strength and Durability Characteristics of Mortars with TiO2 Nanoparticles -- Optimizing Sustainable Construction Materials with Machine Learning Algorithms: Predicting Compressive Strength of Concrete Composites -- Group Indexing of Fly Ashes using Unsupervised -- Learning and Fuzzy Clustering Techniques -- Study on Self-Healing Properties of Bacteria based Cement Mortar with Eggshell Powder and Jute fibre -- A Study on Repair Effectiveness of Damaged RC Beams with Circular Openings Using CFRP Sheet -- ELA and AAR Dynamics of Glaciers in Chandra Basin, Western Himalayas -- GIS Applications and Machine Learning Approaches in Civil Engineering -- Structural Health Monitoring by Simultaneous Measurement of Strain and Temperature Using Different Materials.  aThis book presents select proceedings of the International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development (IACESD 2023). The topics covered include geographic information systems (GIS) and building information modeling (BIM), integration of numerical methods for fluid flow modeling, and the revolutionary potential of 3D printing within the construction industry. This book serves as a resource material for researchers and industry professionals interested in developing solutions for sustainable and resilient infrastructure that aims for communities with Net Zero Targets. 0aBuildingsxDesign and construction. 0aEnvironmental engineering. 0aCivil engineering. 0aConstruction industryxManagement.96606214aBuilding Construction and Design.24aEnvironmental Civil Engineering.24aConstruction Management.9660631 aMenon, N. Vinod Chandra.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9740041 aKolathayar, Sreevalsa.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9698471 aRodrigues, Hugo.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9740051 aSreekeshava, K. S.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9698492 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981970071408iPrinted edition:z978981970073808iPrinted edition:z9789819700745 0aLecture Notes in Civil Engineering,x2366-2565 ;v45940uhttps://doi.org/10.1007/978-981-97-0072-1  aZDB-2-ENG  aZDB-2-SXE  c58662d58662