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Buildings
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Intelligent Design, Green Construction, and Innovation
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Intelligent Design, Green Construction, and Innovation

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3387

Special Issue Editors

Prof. Dr. Yongping Yu

E-Mail Website
Guest Editor
College of Construction Engineering, Jilin University, Changchun 130021, China
Interests: structural stability; nonlinear vibration; steel-tube concrete
Special Issues, Collections and Topics in MDPI journals
Dr. Zhonghai Xu

E-Mail Website
Guest Editor
National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150080, China
Interests: structural sensitivity analysis; composite material; interface mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Shaopeng Zheng

E-Mail Website
Guest Editor
College of Construction Engineering, Jilin University, Changchun 130021, China
Interests: structural sensitivity analysis; structural topology analysis; metal 3D printing

Special Issue Information

Dear Colleagues,

Intelligent design, green construction, and innovation are highly regarded topics in the field of architecture, encompassing the utilization of smart technologies to improve architectural design, environmentally friendly construction practices, and fostering innovation. This includes the application of artificial intelligence, big data, and the Internet of Things to achieve intelligent design (such as building information modeling techniques), the utilization of novel materials (such as recycled concrete, FRP reinforcement, phase change materials), and methods (such as 3D printing) to achieve green construction, as well as innovative approaches and practices (such as smart building systems) to drive the construction industry towards digitization, automation, and sustainability. This Special Issue of Buildings will focus on the latest developments and technological applications in these areas, promoting the widespread adoption and continuous innovation of intelligent technologies in the field of architecture.

Prof. Dr. Yongping Yu
Dr. Zhonghai Xu
Dr. Shaopeng Zheng
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 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 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

  • intelligent design
  • green construction
  • novel materials
  • 3D printing
  • building structures
  • sustainable development

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

Published Papers (4 papers)

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Research

17 pages, 10156 KiB  
Article
Research on Mechanical Characteristics of High-Toughness Anti-Slip Pile Based on Slope Anti-Slip Stability Enhancement
by Changzhu Xing, Yanwei Yang, Chuanfeng Zheng, Dayu Liu, Haigang Li, Liying Guo, Weitao Lin and Chengda Wang
Buildings 2024, 14(11), 3641; https://doi.org/10.3390/buildings14113641 - 15 Nov 2024
Viewed by 548
Abstract
Aiming at the problem of insufficient slope stability in deep foundation pit engineering, this paper takes the integrated urban and rural water supply project in Lingao County as the research object, simulates and analyzes the landslide process of the slope by using the [...] Read more.
Aiming at the problem of insufficient slope stability in deep foundation pit engineering, this paper takes the integrated urban and rural water supply project in Lingao County as the research object, simulates and analyzes the landslide process of the slope by using the strength discount method, and explores the mechanical response characteristics of the anti-slip piles in depth. It is found that the traditional anti-slip pile is prone to early failure due to bending and tensile damage in the middle of the pile back, which leads to the decline of slope stability. For this reason, this paper designs and studies the high-toughness anti-slip pile material and carries out numerical simulation analyses on C30 concrete anti-slip piles and high-toughness concrete anti-slip piles, respectively, for 9 working conditions, for a total of 18 working conditions. The results show that the bending and tensile toughness and strength of the anti-slip piles are significantly improved by using high-toughness material, which effectively avoids bending and tensile damage, and the slope safety coefficient is increased by 32.10%. Furthermore, the optimized design of anti-slip piles in terms of material, pile length, and pile position can effectively improve the stability of slopes and prolong the service life of the anti-slip piles, which provides a new way of thinking and methodology for the safety design of the deep foundation pit project. Thus, this study has important theoretical significance and engineering application value. Full article
(This article belongs to the Special Issue Intelligent Design, Green Construction, and Innovation)
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24 pages, 4102 KiB  
Article
Plastic Constitutive Training Method for Steel Based on a Recurrent Neural Network
by Tianwei Wang, Yongping Yu, Haisong Luo and Zhigang Wang
Buildings 2024, 14(10), 3279; https://doi.org/10.3390/buildings14103279 - 16 Oct 2024
Cited by 2 | Viewed by 850
Abstract
The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive [...] Read more.
The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively. Full article
(This article belongs to the Special Issue Intelligent Design, Green Construction, and Innovation)
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15 pages, 5189 KiB  
Article
A New Continuous Strength Method for Prediction of Strain-Hardening Performance of High-Strength Aluminum Alloy Cylindrical Columns
by Lihui Chen, Weikai Xu, Yusong Chen and Weipeng Sun
Buildings 2024, 14(10), 3055; https://doi.org/10.3390/buildings14103055 - 25 Sep 2024
Viewed by 652
Abstract
This paper aims to develop a new continuous strength method (CSM) to more accurately predict the strain-hardening characteristics of high-strength aluminum alloy circular hollow sections (CHSs) under axial compression. A total of 11 stub column specimens made of 7A04-T6 and 6061-T6 aluminum alloys [...] Read more.
This paper aims to develop a new continuous strength method (CSM) to more accurately predict the strain-hardening characteristics of high-strength aluminum alloy circular hollow sections (CHSs) under axial compression. A total of 11 stub column specimens made of 7A04-T6 and 6061-T6 aluminum alloys underwent testing. Additionally, 16 sets of experiment data were gathered from open sources, encompassing various aluminum alloy types such as 6082-T6, 6061-T6, and 6063-T5. Validated by experimental result, the finite element (FE) model was applied in a series of comprehensive parameter studies, supplementing the limited test result of high-strength aluminum alloy stub columns. Based on the experiment and FE results, this paper proposes a new CSM relation to determine the cross-sectional resistance of high-strength non-slender CHS aluminum alloys under compression. The cross-sectional resistance obtained from tests are compared with predicted strengths determined using the European code, as well as the solution of the CSM proposed in a previous study and in this paper. The comparison illustrates that the strength predictions in the European code and the previous study are conservative. Compared with the European code and the previous study, the strength prediction formula proposed in this paper improves accuracy by 11% and 5%, respectively, while reducing scatter by 8.4% and 2%, respectively. Full article
(This article belongs to the Special Issue Intelligent Design, Green Construction, and Innovation)
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15 pages, 6390 KiB  
Article
Effect of Steel Support Cross-Section and Preloaded Axial Force on the Stability of Deep Foundation Pits
by Yang Jin, Hanzhe Zhao, Chuanfeng Zheng, Jian Liu and Chong Ding
Buildings 2024, 14(8), 2532; https://doi.org/10.3390/buildings14082532 - 16 Aug 2024
Cited by 3 | Viewed by 760
Abstract
To investigate the effects of steel support cross-section dimensions and preloaded axial force levels on the stability of foundation pits, numerical simulations were conducted for open-cut deep foundation pits based on monitoring data from Changchun Metro Line 9. Results show that increasing the [...] Read more.
To investigate the effects of steel support cross-section dimensions and preloaded axial force levels on the stability of foundation pits, numerical simulations were conducted for open-cut deep foundation pits based on monitoring data from Changchun Metro Line 9. Results show that increasing the wall thickness and diameter of the steel support significantly reduces the horizontal displacement and axial force of the enclosure pile. When the wall thickness increases from 14 mm to 25 mm, the horizontal displacement of the enclosure pile can be reduced by up to 7.63 mm, and the axial force of the steel support can be reduced by 11.4–15%. When the diameter of the steel support changes from 609 mm to 800 mm, the axial force of the second steel support is reduced by 3.2–5.5%. The change in preloaded axial force results in a horizontal displacement change of about 3–5 mm and a surface settlement change of about 0.6–4.2 mm. The preloaded axial force meets pit stability control requirements when it reaches 60% of the design axial force. Full article
(This article belongs to the Special Issue Intelligent Design, Green Construction, and Innovation)
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