Aims and Scope

The Open Construction & Building Technology Journal is an Open Access online journal which publishes original research articles, review/mini-review articles, short articles (letters) and guest edited single topic issues in all areas of construction and building technology. The journal encourages submissions related to the following fields of construction and building technology:


  • New Works and Repair /Maintenance of All Civil Engineering Structures
  • Cement, Concrete Reinforcement, Bricks and Mortars
  • Modeling of mechanical properties of structural materials
  • Soft Computing Techniques in Structural Engineering and Materials
  • Structural design, diagnostics, and health monitoring
  • Additives
  • Corrosion Technology
  • Ceramics
  • Timber
  • Steel
  • Polymers
  • Glass Fibres

The Open Construction & Building Technology Journal , a peer reviewed journal, is an important and reliable source of current information on important developments and research in the field. The emphasis will be on publishing quality papers rapidly and making them freely available to researchers worldwide.


Recent Articles

Static Performances of Timber- and Bamboo-Concrete Composite Beams: A Critical Review of Experimental Results

Simret T. Deresa, Jinjun Xu, Cristoforo Demartino, Giovanni Minafò, Gaetano Camarda

The use of composite beams made with traditional concrete and bio-based materials (such as timber and bamboo) is a valuable solution to reduce the environmental impact of the building sector. Timber-Concrete Composite (TCC) beams have been used for decades in structural applications such as new buildings, refurbishment of old timber structures, and bridges. Recently, different researchers suggested composite beams based on engineered bamboo, commonly named Bamboo-Concrete Composite (BCC) beams. This study presents a systematic comparison of structural performances and connection behavior of TCC and BCC beams under short-term static load. TCCs beams are compared to BCC ones using similar shear connectors. The most important aspects of the two composite systems are compared: mechanical behavior of connectors and structural behaviors of full-scale composite beams (e.g., failure modes, connection stiffness, connection shear strength, ultimate load-carrying capacity, maximum deflection and composite efficiency). This comprehensive review indicates that BCC beams have similar or even better structural performances compared with TCC.


March 31, 2021
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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
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Indexing Agencies

ScopusChemical Engineering and Biotechnology Abstracts (CEABA-VTB)J-GateGenamics JournalSeekMediaFinder®-Standard Periodical DirectoryCNKI ScholarROAD Directory
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