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Applied Sciences
Special Issues
Intelligent Construction: Advancements in Civil Engineering and Building Structures
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Intelligent Construction: Advancements in Civil Engineering and Building Structures

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1624

Special Issue Editors

Dr. Sandra Pereira

E-Mail Website
Guest Editor
School of Science and Technology, University of Trás-os-Montes e Alto Douro, Portugal C-MADE / UTAD, Quinta de Prados, 5000 Vila Real, Portugal
Interests: energy efficiency; buildings sustainability; sustainable materials; construction economics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. António Cunha

E-Mail Website
Guest Editor
Department of Engineering, University of Trás-os-Montes and Alto Douro and INESC TEC, 5000-801 Vila Real, Portugal
Interests: medical image analysis; bio-image analysis; computer vision; image and video processing; machine learning; artificial intelligence, with a focus on the application of computer-aided diagnosis across various imaging modalities, including ophthalmology, endoscopic capsule videos and the computed tomography of the lung
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The concept of intelligent construction (IC) has emerged as an innovative approach to revolutionize the architecture, engineering, and construction (AEC) industry by integrating advanced information technologies such as artificial intelligence (AI) and the Internet of Things (IoT).

We are seeking valuable contributions to our Special Issue, “Intelligent Construction: Progress in Civil Engineering and Architectural Innovations”. Within this Special Issue, we aim to showcase the latest developments and innovations that are resha** the field of civil engineering and construction.

We invite experts to share their expertise and research findings with a global audience. We encourage submissions that explore the far-reaching effects of advanced information technologies, including AI and IoT, on the construction sector, whether they involve enhancing project efficiency, sustainability, design methodologies, safety protocols, or any other facet of intelligent construction.

Topics of interest include the following:

  • Data fusion and decision-making;
  • Improving the accuracy and efficiency of knowledge representation, learning, and utilization;
  • Establishing large pre-trained models in the field;
  • Embodied AI for action based on decisions.

By submitting your work to this Special Issue, you are participating in sha** the future of civil engineering. We believe your contributions will inspire and inform our readers, making intelligent construction the cornerstone of sustainable, efficient, and safe building structures.

Join us in this journey of discovery as we unveil the advancements driving the future of civil engineering and building structures. We look forward to receiving your contributions.

Dr. Sandra Pereira
Dr. António Cunha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent construction
  • advanced information technologies
  • data fusion and decision-making
  • embodied AI
  • sustainable building structures

Published Papers (2 papers)

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Research

21 pages, 3939 KiB  
Article
Course of Cumulative Cost Curve (CCCC) as a Method of CAPEX Prediction in Selected Construction Projects
by Mariusz Szóstak, Tomasz Stachoń and Jarosław Konior
Appl. Sci. 2024, 14(13), 5597; https://doi.org/10.3390/app14135597 - 27 Jun 2024
Viewed by 225
Abstract
Forecasting the actual cost of the implementation of a construction project is of great importance in the case of technical management and enables financial resources to be initially maintained in a controlled manner and in a way that is as close as possible [...] Read more.
Forecasting the actual cost of the implementation of a construction project is of great importance in the case of technical management and enables financial resources to be initially maintained in a controlled manner and in a way that is as close as possible to the actual state. Based on the analysis of the developed knowledge base, which contains data from 612 reports of the Bank Investment Supervision regarding 45 construction projects from 2006 to 2023 with a total value of over PLN 1,300,000,000, best-fit curves were determined, and the expected area of the cumulative actual cost of selected construction projects was specified. The obtained polynomial functions and graphs of real areas of cost curves (in the form of nomograms) constitute a reliable graphical representation that enables the application of research results in typologically similar groups/sectors of the construction industry. The elaborated course of the cumulative cost curve (CCCC) as a method of CAPEX prediction in selected construction projects stands for a combined approach of the S-curve, polynomial functions, and the best-fit area of cumulative earned cost. The research used scientific tools that can be practically and easily used by both managers and participants of the investment process. Full article
(This article belongs to the Special Issue Intelligent Construction: Advancements in Civil Engineering and Building Structures)
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18 pages, 1765 KiB  
Article
A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks—Using YOLO v8 and Dataset Review
by Joel de Conceição Nogueira Diniz, Anselmo Cardoso de Paiva, Geraldo Braz Junior, João Dallyson Sousa de Almeida, Aristófanes Corrêa Silva, António Manuel Trigueiros da Silva Cunha and Sandra Cristina Alves Pereira da Silva Cunha
Appl. Sci. 2024, 14(10), 4332; https://doi.org/10.3390/app14104332 - 20 May 2024
Viewed by 783
Abstract
Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. [...] Read more.
Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy. Full article
(This article belongs to the Special Issue Intelligent Construction: Advancements in Civil Engineering and Building Structures)
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