Aims and Scope
Recent Articles
Recent Advances and New Discussions on Superhydrophobic Coatings and Admixtures Applied to Cementitious Materials
Laísa do Rosário Souza Carneiro, Manuel Houmard, Péter Ludvig
Increasing the durability of buildings is one of the biggest challenges of the construction industry of the 21st century. The problems concerning durability are usually related to the presence of humidity or to water infiltration in the porous cementitious materials used in buildings. Advances in biomimetics have allowed the development of superhydrophobic surfaces and materials, with contact angles greater than 150°, which are able to repel water and aqueous products. In this context, this work summarizes the recent advances on superhydrophobic coatings and admixtures applied to cementitious materials. Recommendations for the future improvement of such products are made. The synthesis of superhydrophobic coatings generally includes the deposition of a low surface energy material (LSEM), especially fluoroalkylsilanes, on a microroughened surface, which, in cementitious materials, is usually achieved with the help of nanoparticles or micrometric molds. In this sense, variables as the spraying time duration, and the nanoparticles concentration, surface area and average particle size were identified as directly influencing the surface superhydrophobicity. Functionalized nanoparticles can also be introduced in cement matrix during the paste mixing in order to obtain a longer lasting waterproofing effect. In this case, hybrid nanosilica may react with Ca(OH)2 through pozzolanic reaction. The C-S-H formed may incorporate the organic group of hybrid nanosilica, and might present superhydrophobicity as well, modifying the composite’s microstructure. Besides, the cost of fabricating hydrophobic materials is decisive for their market entry. Hence, the partial or total replacement of fluoroalkylsilanes with less expensive LSEMs seems promising and needs to be further explored.
December 31, 2020
Articles
- December 23, 2020
Development of a Performance Concept in the Construction Field: A Critical Review
November 27, 2020
Structural Performance of Straw Block Assemblies under Compression Load
October 26, 2020
Proactive Actions Based on a Resilient Approach to Urban Seismic Risk Mitigation
August 24, 2020Experimental Behavior of Concrete Columns Confined by Transverse Reinforcement with Different Details
August 19, 2020
The Design of a Real-Scale Steel Moment-Resisting Frame for Pseudo-Dynamic Earthquake Testing
July 30, 2020
Additive Manufacturing in the Geopolymer Construction Technology: A Review
Editor's Choice
Non-destructive Method of the Assessment of Stone Masonry by Artificial Neural Networks
Rachel Martini, Jorge Carvalho, António Arêde, Humberto Varum
Background:
In this study , a methodology based on non-destructive tests was used to characterize historical masonry and later to obtain information regarding the mechanical parameters of these elements. Due to the historical and cultural value that these buildings represent, the maintenance and rehabilitation work are important to maintain the appreciation of history. The preservation of buildings classified as historical-cultural heritage is of social interest, since they are important to the history of society. Considering the research object as a historical building, it is not recommended to use destructive investigative techniques.
Objective:
This work contributes to the technical-scientific knowledge regarding the characterization of granite masonry based on geophysical, mechanical and neural networks techniques.
Methods:
The database was built using the GPR (Ground Penetrating Radar) method, sonic and dynamic tests, for the characterization of eight stone masonry walls constructed in a controlled environment. The mechanical characterization was performed with conventional tests of resistance to uniaxial compression, and the elastic modulus was the parameter used as output data of ANNs.
Results:
For the construction and selection of network architecture, some possible combinations of input data were defined, with variations in the number of hidden layer neurons (5, 10, 15, 20, 25 and 30 nodes), with 122 trained networks.
Conclusion:
A mechanical characterization tool was developed applying the Artificial Neural Networks (ANN), which may be used in historic granite walls. From all the trained ANNs, based on the errors attributed to the estimated elastic modulus, networks with acceptable errors were selected.
May 23, 2020
Other Post
- December 31, 2019
Investigation of Uplift Capacity of Deep Foundation in Various Geometry Conditions
August 30, 2019
Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis
March 28, 2019
An Enhanced Beam Model for the Analysis of Masonry Walls
August 30, 2019
The Behavior of Concrete-Filled Single and Double-Skin uPVC Tubular Columns Under Axial Compression Loads
January 31, 2019
Seismic Assessment of Steel MRFs by Cyclic Pushover Analysis
November 23, 2018
Empirical Model Of Unreinforced Beam-column RC Joints With Plain Bars
