检索结果-南通市图书馆

The effects of mismatch fracture properties in encapsulation-based self-healing concrete using cohesive-zone model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2020年第3期14卷 792-801页

作者： Luthfi Muhammad MAULUDIN Chahmi OUCIF Timon RABCZUK Institute of Structural Mechanics Bauhaus University of WeimarWeimar 99423Germany Department of Civil Engineering Politebiik Negeri Bandung(POLBAN)Bandung 40012Indonesia Division of Computational Mechanics Ton Due Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Tori Due Thang UniversityHo Chi Minh CityVietnam

Finite element analysis is developed to simulate the breakage of capsule in capsule-based self-healing concrete.A 2D circular capsule with different core-shell thickness ratios embedded in the mortar matrix is analyzed numerically along with their interfacial transition zone.Zero-thickness cohesive elements are pre-inserted into solid elements to represent potential cracks.This study focuses on the effects of mismatch fracture properties,namely fracture strength and energy,between capsule and mortar matrix into the breakage likelihood of the capsule.The extensive simulations of 2D specimens under uniaxial tension were carried out to investigate the key features on the fracture patterns of the capsule and produce the fracture maps as the results.The developed fracture maps of capsules present a simple but valuable tool to assist the experimentalists in designing appropriate capsule materials for self-healing concrete.

关键词： self-healing concrete interfacial zone capsule materials cohesive elements fracture maps

Computational modeling of fracture in capsule-based self-healing concrete: A 3D study

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2021年第6期15卷 1337-1346页

作者： Luthfi Muhammad MAULUDIN Timon RABCZUK Civil Engineering Department Gegerkalong Hilir Ds.CiwarugaBandung 40012Indonesia Institute of Structural Mechanics Bauhaus University of WeimarWeimar 99425Germany

We present a three-dimensional(3D)numerical model to investigate complex fracture behavior using cohesive elements.An efficient packing algorithm is employed to create the mesoscale model of heterogeneous capsulebased self-healing concrete.Spherical aggregates are used and directly generated from specified size distributions with different volume fractions.Spherical capsules are also used and created based on a particular diameter,and wall thickness.Bilinear traction-separation laws of cohesive elements along the boundaries of the mortar matrix,aggregates,capsules,and their interfaces are pre-inserted to simulate crack initiation and propagation.These pre-inserted cohesive elements are also applied into the initial meshes of solid elements to account for fracture in the mortar matrix.Different realizations are carried out and statistically analyzed.The proposed model provides an effective tool for predicting the complex fracture response of capsule-based self-healing concrete at the meso-scale.

关键词： 3D fracture self-healing concrete spherical cohesive elements heterogeneous

The Prediction of self-healing Capacity of Bacteria-Based concrete Using Machine Learning Approaches

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2019年第4期59卷 57-77页

作者： Xiaoying Zhuang Shuai Zhou Division of Computational Mechanics Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityVietnam College of Materials Science and Engineering Chongqing UniversityChongqing400045China Institute of Continuum Mechanics Leibniz University HannoverHannover30167Germany

Advances in machine learning(ML)methods are important in industrial engineering and attract great attention in recent years.However,a comprehensive comparative study of the most advanced ML algorithms is lacking.Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared.Six ML algorithms,including the Support Vector Regression(SVR),Decision Tree Regression(DTR),Gradient Boosting Regression(GBR),Artificial Neural Network(ANN),Bayesian Ridge Regression(BRR)and Kernel Ridge Regression(KRR),are adopted for the relationship modeling to predict crack closure percentage(CCP).Particle Swarm Optimization(PSO)is used for the hyper-parameters tuning.The importance of parameters is analyzed.It is demonstrated that integrated ML approaches have great potential to predict the CCP,and PSO is efficient in the hyperparameter tuning.This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering.

关键词： Bacteria self-healing concrete crack closure percentage machine learning prediction

Multifunctional, Sustainable, and Biological Non-Ureolytic self-healing Systems for Cement-Based Materials

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Engineering 2022年第6期8卷 217-237页

作者： Mohammad Fahimizadeh Pooria Pasbakhsh Lee Sui Mae Joash Ban Lee Tan R.K.Singh Raman School of Engineering Monash University MalaysiaBandar Sunway 47500Malaysia School of Science Monash University MalaysiaBandar Sunway 47500Malaysia Tropical Medicine and Biology Multidisciplinary Platform Monash University MalaysiaBandar Sunway 47500Malaysia Department of Chemical Engineering&Department of Mechanical and Aerospace Engineering Monash UniversityClaytonVIC 3168Australia

Microbially induced calcium carbonate(CaCO_(3))precipitation(MICP)has been investigated as a sustain-able alternative to conventional concrete remediation methods for improving the mechanical properties and durability of concrete structures.To date,urea-dependent MICP is the most widely employed MICP pathway in biological self-healing concrete research as its use has resulted in efficient CaCO_(3) precipita-tion rates.NH_(3) is a byproduct of ureolysis,and can be hazardous to cementitious structures and the health of various species.Accordingly,non-ureolytic bacterial concrete self-healing systems have been developed as eco-friendly alternatives to urea-dependent self-healing systems.Non-ureolytic pathways can improve the physical properties of concrete samples and incorporate the use of waste materials;they have the potential to be cost-effective and sustainable.Moreover,they can be applied in terrestrial and marine environments.To date,research on non-ureolytic concrete self-healing systems has been scarce compared to that on ureolytic systems.This article discusses the advances and challenges in non-ureolytic bacterial concrete self-healing studies and highlights the directions for future research.

关键词： self-healing concrete Mechanical properties Durability Non-ureolytic pathways Cement composite Sustainability

Effects of hydrogel-encapsulated bacteria on the healing efficiency and compressive strength of concrete

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Road Engineering 2023年第2期3卷 156-170页

作者： Ricardo Hungria Marwa M.Hassan Momen Mousa Bert S.Turner Department of Construction Management Louisiana State UniversityBaton RougeLA 70803USA Department of Engineering Technology Sam Houston State UniversityHuntsvilleTX 77341USA

Microbial-induced calcium carbonate precipitation is a promising technology for self-healing concrete due to its capability to seal microcracks.The main goal of this study was to evaluate the effects of adding hydrogelencapsulated bacteria on the compressive strength and the self-healing efficiency of concrete.To achieve this objective,12 sets of mortar samples were prepared,including three different mineral precursors(magnesium acetate,calcium lactate,and sodium lactate),at two concentrations(67.76 and 75.00 mM/L),and under two different biological conditions(with and without bacteria).In addition,a set of plain mortar samples was prepared to serve as a control.For each sample set,three mortar cubes and three beams were prepared and subjected to compression and flexural strength tests.From the compression tests,it was found that the sample containing calcium lactate along with yeast extract and bacteria displayed the best results.As for the flexural tests,once cracked,the beams were subjected to 28 d of wet/dry cycles(16 h of water immersion and 8 h of drying),where the bottom crack width was monitored(at 0,3,7,14,28 d of wet/dry cycles).Once the sample with the highest healing efficiency was identified(the one containing calcium lactate and hydrogel-encapsulated bacteria),the study was scaled up to concrete specimens.Two sets of concrete cylinders(consisting of three control samples and three samples with bacteria along with calcium lactate)were tested under compression in order to evaluate the effect of the bacteria-precursor combination on the concrete mechanical properties.The samples that yielded the greatest compressive strength were the ones containing calcium lactate and bacteria,displaying an improvement of 17%as compared to the control specimen.Furthermore,a flexural strength recovery analysis was performed on the concrete specimens revealing that the control showed better flexural strength recovery than the bacteriacontaining variant(41.5%vs.26.1%)after 28 d of wet/dry cycles.A healing efficiency anal

关键词： self-healing concrete Bioconcrete Microbial induced calcium carbonate precipitation Precursor