Forthcoming and Online First Articles

International Journal of Structural Engineering

International Journal of Structural Engineering (IJStructE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Structural Engineering (2 papers in press)

Regular Issues

  • Study on the mechanical behaviour of assembly prestressed reinforced structure and shield lining composite system   Order a copy of this article
    by Fengbin Su, Yi Liu, Kai Wang, Huan Pang, Meng Chen 
    Abstract: Internal reinforcement technology is an important method for controlling deformation of shield tunnels However, existing internal reinforcement technologies fail to balance strengthening efficiency with economic feasibility, which also hinder the detection of cracks and water leakage This paper innovatively proposes a design approach of assembly prestressed reinforced structure (APRS) to solve the existing problems A full-size numerical model of APRS and shield lining composite system is established by using ABAQUS In the selection of prestressed types, the reinforcement efficiency is the highest when the vault member is set to tensile stress and the arch waist member is set to compressive stress Regarding the determination of prestressing magnitude, strengthening efficiency is proportional to the prestressing value, and the optimal limit of prestress setting is 50 MPa Moreover, strengthening efficiency of unit steel quantity of the APRS is 2 2 times that of the steel bonding reinforcement structure, demonstrating superior economic efficiency.
    Keywords: assembly prestressed reinforced structure; strengthening efficiency; upgrade rate; mechanical behavior.

  • Towards identification of long-term building defects using transfer learning   Order a copy of this article
    by Aravinda Boovaraghavan, Christy Jackson Joshua, Abdul Quadir Md, Kong Fah Tee, V. Sivakumar 
    Abstract: Detecting long-term issues on various types of building wall surfaces, such as cracks, flakes, and roofs, is vital for timely maintenance and repairs before they become too risky and expensive. Currently, building managers manually assess the building conditions to survey and communicate the state of their buildings. However, this manual process is subjective, often leads to inaccuracies, and is time-consuming; thus, it needs to be more efficient. These flaws can severely influence a buildings structural stability if they go undiscovered and ignored. In this context, this study proposes an approach named towards identification of long-term building defects using transfer learning (TILT) to identify unnoticed defects such as cracks, flakes, and roofs robustly and accurately in buildings. The proposed model has been tested using images taken from real-world onsite deployments, and the types of construction issues have been determined with 98.33% accuracy predicted by the VGG16 model and 79.13% accuracy predicted by the ResNet50 model. Overall, the VGG16 model gives better results compared to ResNet50.
    Keywords: structural health monitoring; damage detection; building defects; classification; transfer learning; VGG16; ResNet50.

 

Return to top