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Advanced Research on Intelligent Building Construction and Management
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Advanced Research on Intelligent Building Construction and Management

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6243

Special Issue Editors

Dr. Lin Liu

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: urban climatic prediction; human thermal comfort evaluation; environmental suitability assessment; adaptation analysis of management decisions
Special Issues, Collections and Topics in MDPI journals
Dr. Taotao Shui

E-Mail Website
Guest Editor
School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
Interests: urban microclimate; urban energy budget; urban pollutant dispersion; thermal comfort; carbon emission
Dr. Chun Wang

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Chongqing University of Science Technology, Chongqing 401331, China
Interests: building informatization; low-carbon smart buildings; virtual restoration of traditional buildings; information management of engineering projects

Special Issue Information

Dear Colleagues,

The development of intelligent construction is a core driving force to break through industry bottlenecks and accelerate construction industry transformation for the future. Intelligent construction integrates a series of advanced technologies and involves many areas of expertise in civil engineering, computer application, engineering management, mechanical automation, electrical power systems, clean energy, and other fields of knowledge. It should be noted that intelligent construction is inseparable from intelligent operation and maintenance. Reasonable management methods and operational control strategies significantly contribute to the construction industry with efficiency and low-carbon strategies resulting in comprehensive, coordinated, and sustainable development. Therefore, intelligent construction should consider various factors to obtain a balance between economic and environmental comfort. This special issue welcomes all advanced theories and technologies related to intelligent construction, including but not limited to the following topics:

  • Project management knowledge;
  • Management decision making;
  • BIM technology;
  • HSE evaluation;
  • Prefabricated building technology;
  • Green building technology;
  • Building big data;
  • Artificial intelligence;
  • Smart cities;
  • Intelligent energy use management;
  • Multi-scale information databases;
  • Other new technologies in communities, buildings, cities, industry parks, etc.

Dr. Lin Liu
Dr. Taotao Shui
Dr. Chun Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • artificial intelligence
  • project management
  • decision-making method
  • intelligent buildings
  • building information modeling
  • smart cities
  • intelligent management
  • low-carbon strategy
  • intelligent power technology
  • intelligence algorithm

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

16 pages, 2247 KiB  
Article
Semantic Segmentation of Heavy Construction Equipment Based on Point Cloud Data
by Suyeul Park and Seok Kim
Buildings 2024, 14(8), 2393; https://doi.org/10.3390/buildings14082393 - 2 Aug 2024
Viewed by 930
Abstract
Most of the currently developed 3D point cloud data-based object recognition algorithms have been designed for small indoor objects, posing challenges when applied to large-scale 3D point cloud data in outdoor construction sites. To address this issue, this research selected four high-performance deep [...] Read more.
Most of the currently developed 3D point cloud data-based object recognition algorithms have been designed for small indoor objects, posing challenges when applied to large-scale 3D point cloud data in outdoor construction sites. To address this issue, this research selected four high-performance deep learning-based semantic segmentation algorithms for large-scale 3D point cloud data: Rand-LA-Net, KPConv Rigid, KPConv Deformable, and SCF-Net. These algorithms were trained and validated using 3D digital maps of earthwork sites to build semantic segmentation models, and their performance was tested and evaluated. The results of this research represent the first application of 3D semantic segmentation algorithms to large-scale 3D digital maps of earthwork sites. It was experimentally confirmed that object recognition technology can be implemented in the construction industry using 3D digital maps composed of large-scale 3D point cloud data. Full article
(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)
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18 pages, 3336 KiB  
Article
Research on Arrangement of Measuring Points for Modal Identification of Spatial Grid Structures
by Chunjuan Zhou, Jinzhi Wu, Guojun Sun, Jie Hu, Qize Xu, Yang Li and Mingliang Liu
Buildings 2024, 14(8), 2338; https://doi.org/10.3390/buildings14082338 - 28 Jul 2024
Viewed by 782
Abstract
In structural health monitoring, because the number of sensors used is far lower than the number of degrees of freedom of the structure being monitored, the optimization problem of the location and number of sensors in the structures is becoming more and more [...] Read more.
In structural health monitoring, because the number of sensors used is far lower than the number of degrees of freedom of the structure being monitored, the optimization problem of the location and number of sensors in the structures is becoming more and more prominent. However, spatial grid structures are complex and diverse, and their dynamic characteristics are complex. It is difficult to accurately measure their vibration information. Therefore, an appropriate optimization method must be used to determine the optimal positioning of sensor placement. Aiming at the problem that spatial grid structures have many degrees of freedom and the fact that it is difficult to obtain complete vibration information, this paper analyzed the typical EI method, MKE method, and EI-MKE method in the arrangement of the measuring points, and it was verified that the EI method was more suitable for the vibration detection of spatial grid structures through the example of a plane truss and spatial grid structures. Measuring points under the assumption of structural damage were explored, and it was proposed that there might have been a stable number of measuring points that could cover the possible vibration mode changes in the structures. At the same time, combined with the three-level improved Guyan recursive technique, in order to obtain better complete modal parameters, the influence of the number of measuring points on the complete vibration mode information was studied. It was concluded that MACd was better than MACn as the quantitative target. Full article
(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)
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16 pages, 8270 KiB  
Article
Numerical Analysis of Dynamic Characteristics of an Asymmetric Tri-Stable Piezoelectric Energy Harvester under Random Vibrations in Building Structures
by Dawei Man, Qingnan Hu, Qinghu Xu, Liping Tang, Dong Chen, Ziqing Yuan and Tingting Han
Buildings 2024, 14(7), 2210; https://doi.org/10.3390/buildings14072210 - 18 Jul 2024
Viewed by 668
Abstract
This study presents a novel design for a tri-stable piezoelectric vibration energy harvester with an asymmetric structure, which is enhanced with an elastic base (TPVEH + EB), meticulously designed to enhance energy extraction from irregular vibrations in architectural structures. The cornerstone of this [...] Read more.
This study presents a novel design for a tri-stable piezoelectric vibration energy harvester with an asymmetric structure, which is enhanced with an elastic base (TPVEH + EB), meticulously designed to enhance energy extraction from irregular vibrations in architectural structures. The cornerstone of this design is the asymmetric tri-stable piezoelectric cantilever beam, distinctively arranged within a U-shaped block and fortified with an elastic foundation. A carefully positioned spring (kf)-mass (Mf) system between the U-shaped block and the beam’s fixed end significantly boosts the vertical displacement of the beam during oscillations. Utilizing Lagrange’s equations, we formulated a dynamic model for the asymmetric TPVEH + EB, examining the effects of potential well asymmetry, the stiffness of the elastic base and spring-mass system, the mass of the spring-mass system, and the tip magnet mass on the system’s nonlinear dynamic responses. Our results demonstrate that the asymmetric TPVEH + EB significantly enhances energy harvesting from low-amplitude random vibrations (1.5 g), with the output voltage of the asymmetric TPVEH + EB increasing by 30% and the output power by 25%. Extensive numerical and theoretical analyses verify that the asymmetric TPVEH + EB provides a highly efficient solution for scenarios typically hindered by low energy conversion rates. Its reliable performance under varied and unpredictable excitation conditions highlights its excellence in advanced energy harvesting applications. The improvements detailed in this research underscore the potential of the asymmetric TPVEH + EB to boost energy harvesting efficiency, particularly in powering wireless sensor nodes for structural health monitoring in buildings. By overcoming the limitations of traditional harvesters, the asymmetric TPVEH + EB ensures enhanced efficiency and reliability, making it an ideal solution for a wide range of practical applications in diverse environmental conditions within buildings. Full article
(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)
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24 pages, 8737 KiB  
Article
Adaptability Analysis of Integrated Project Delivery Method in Large- and Medium-Sized Engineering Projects: A FAHP-Based Modeling Solution
by Huiyu He, Xiwei Gan, Lin Liu and Xing Zhang
Buildings 2024, 14(7), 1999; https://doi.org/10.3390/buildings14071999 - 2 Jul 2024
Viewed by 2496
Abstract
With the emerging large- and medium-sized engineering projects, prominent project delivery methods make sense in terms of cost, risk, management, and schedule. Among these, the Integrated Project Delivery (IPD) method stands out due to its adaptability for growing scale and complexity projects. This [...] Read more.
With the emerging large- and medium-sized engineering projects, prominent project delivery methods make sense in terms of cost, risk, management, and schedule. Among these, the Integrated Project Delivery (IPD) method stands out due to its adaptability for growing scale and complexity projects. This study compares the IPD method with other methods, emphasizing its benefits in large- and medium-sized projects and introducing the Fuzzy Analytic Hierarchy Process (FAHP) model to analyze IPD’s adaptability quantitatively. By conducting a matrix calculation of eighteen second-level indicators, this study derived weight values for four first-level indicators: Cost control, Risk control, Management control, and Schedule control. These first-level indicators were then used to formulate the total evaluation index calculation. Based on this foundation, we verified the calculations using a case study in Fujian. Implementing the IPD method led to a lower cost than the Owner’s Representative method and a one-year schedule acceleration. The FAHP model introduced in this study offers a novel and objective approach for adaptability analysis of the IPD method in large- and medium-sized engineering projects, coupling decision theory into project management. Full article
(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)
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17 pages, 11688 KiB  
Article
Analysis of Progressive Collapse Resistance in Precast Concrete Frame with a Novel Connection Method
by Qinghu Xu, Junjie Qian, Yu Zhang, Liping Tang, Dawei Man, Xuezhi Zhen and Tingting Han
Buildings 2024, 14(6), 1814; https://doi.org/10.3390/buildings14061814 - 14 Jun 2024
Cited by 1 | Viewed by 773
Abstract
The configuration of beam–column joints in precast concrete (PC) building structures varies widely, and different connection methods significantly affect the progressive collapse resistance of the structure. This study investigates the progressive collapse resistance of an innovative beam–column connection node frame. Finite element models [...] Read more.
The configuration of beam–column joints in precast concrete (PC) building structures varies widely, and different connection methods significantly affect the progressive collapse resistance of the structure. This study investigates the progressive collapse resistance of an innovative beam–column connection node frame. Finite element models of four-story, two-span space frame structures made of reinforced concrete (RC) and PC were developed using ANSYS 14.0/LS-DYNA R5.x software, employing nonlinear dynamic and static analysis to examine structural collapse behavior under bottom middle or corner column damage. Numerical results indicate that following the failure of the middle or corner column due to explosion loading, the vertical displacement and collapse rate of the PC structure with the novel connection method are less than those of the RC structure during collapse progression. Furthermore, upon removal of the middle or corner column, the residual load-carrying capacity of the PC structure with the innovative connection increased by 7% and 3.7%, respectively, compared to the RC structure. This suggests that PC structures with this type of connection demonstrate superior performance in resisting progressive collapse, offering valuable insights for future engineering applications. Full article
(This article belongs to the Special Issue Advanced Research on Intelligent Building Construction and Management)
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