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Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and...
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Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications

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

Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 8894

Special Issue Editors

Dr. Lei Li

E-Mail Website
Guest Editor
Department of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Interests: post-earthquake assessment of building structures
Special Issues, Collections and Topics in MDPI journals
Dr. Jiantao Wang

E-Mail Website
Co-Guest Editor
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi'an 710055, China
Interests: high-performance concrete-filled steel tubular (CFST) structures; disaster prevention and mitigation of power grid projects
Dr. Yixin Zhang

E-Mail Website
Co-Guest Editor
College of Civil Engineering, Huaqiao University, Xiamen 361021, China
Interests: concrete durability and retrofitting

Special Issue Information

Dear Colleagues,

As is well-known, concrete has played a significant role in human society since its invention. Recent engineering projects, such as the Hong Kong‒Zhuhai‒Macao Bridge, have placed higher performance requirements on materials and structures. In response, emerging techniques, such as ultra-high-performance concrete (UHPC), coral aggregate concrete (CAC), and seawater sea‒sand concrete (SSC), have emerged. Furthermore, the development of low-carbon and eco-friendly concrete is becoming increasingly popular, due to human concerns for their living environment. Evaluating the structural performance while applying these promising techniques in engineering structures is essential. In recent years, machine learning (ML)-based and vision-based methods in structural evaluation have received significant attention and have become supplements to traditional evaluation methods.

This Issue aims to invite high-quality contributions on the emerging techniques in concrete materials and structures. Authors are encouraged to submit original papers presenting new materials or structures, theoretical, and/or application-oriented research, including models, algorithms, and applications. Additionally, review papers on these topics are also welcome. The topics of interest include, but are not limited to:

  • Ultra-high-performance Concrete and Structures;
  • Seawater Sea‒Sand Concrete and Structures;
  • Low-carbon and Eco-friendly Materials and Structures;
  • Machine-learning-based and Computer-vision-based Structural Analysis and Evaluation;
  • New Techniques in Strengthening and Retrofitting of Existing Structures.

Dr. Lei Li
Dr. Jiantao Wang
Dr. Yixin Zhang
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

  • ultra-high-performance concrete
  • seawater sea‒sand concrete
  • low-carbon
  • eco-friendly
  • AI-based
  • structural evaluation
  • existing structures

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

Published Papers (8 papers)

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Research

Jump to: Review

16 pages, 3615 KiB  
Article
Analyzing the Impact of Deep Excavation on Retaining Structure Deformation Based on Element Tracking
by Wen Tan, Zhenyu Lei, Yanhong Wang, Jinsong Liu, Pengbang Lai, Yuan Mei, Wenzhan Liu and Dongbo Zhou
Buildings 2024, 14(10), 3069; https://doi.org/10.3390/buildings14103069 - 25 Sep 2024
Viewed by 662
Abstract
In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the [...] Read more.
In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the use of element tracking. By duplicating elements for temporary supports or structures requiring changes in material properties and appropriately activating or deactivating them at the right moments, the simulation of the foundation pit excavation process can be achieved more precisely. Using the construction process of the Tangxi Passenger Transport Station’s comprehensive transportation hub foundation pit as an example, this study applied the proposed simulation method and compared the results with actual measurements, demonstrating its effectiveness. This research offers a more accurate approach for simulating foundation pit excavation and provides a reference for similar numerical simulation problems. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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16 pages, 3587 KiB  
Article
Time-Varying Stability Analysis of the Trenching Construction Process of Diaphragm Wall
by Zhicheng Liu, Jianmei Liu, Muyu Li, Wufeng Mao, Ran Wang, Yuan Mei, Wenzhan Liu and Dongbo Zhou
Buildings 2024, 14(10), 3038; https://doi.org/10.3390/buildings14103038 - 24 Sep 2024
Viewed by 495
Abstract
The stability of underground diaphragm walls is crucial for ensuring the safety and integrity of trench excavations in geotechnical engineering. This study addresses this critical issue by proposing a novel destabilization mechanism based on a sliding body model specifically designed for diaphragm wall [...] Read more.
The stability of underground diaphragm walls is crucial for ensuring the safety and integrity of trench excavations in geotechnical engineering. This study addresses this critical issue by proposing a novel destabilization mechanism based on a sliding body model specifically designed for diaphragm wall trenching operations. The research employs an analytical framework rooted in soil mechanics and plasticity theory, utilizing limit equilibrium analysis to develop a method for calculating the minimum required slurry density and corresponding safety factor for trench stability. The study compares two distinct approaches to slurry density computation, analyzing their sensitivity to various influencing factors. Theoretical findings are validated through multiple real-world engineering case studies. Comparative analysis demonstrates the superiority of the proposed method, particularly in assessing trench stability within clay layers. Key variables influencing the safety factor are identified, including trench length, slurry density, soil friction angle, and the relative height difference between slurry and groundwater levels. Results indicate that actual slurry densities observed in practice consistently fall within the bounds predicted by the theoretical calculations. This research contributes a valuable theoretical framework to the field of diaphragm wall construction, offering improved accuracy in stability assessments and potentially enhancing safety in geotechnical engineering projects. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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22 pages, 6582 KiB  
Article
Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks
by Zhenyu Lei, Yanhong Wang, Yu Zhang, Feng Gu, Zihui Zan, Yuan Mei, Wenzhan Liu and Dongbo Zhou
Buildings 2024, 14(9), 3022; https://doi.org/10.3390/buildings14093022 - 23 Sep 2024
Viewed by 531
Abstract
This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present [...] Read more.
This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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16 pages, 2933 KiB  
Article
A Two-Level Machine Learning Prediction Approach for RAC Compressive Strength
by Fei Qi and Hangyu Li
Buildings 2024, 14(9), 2885; https://doi.org/10.3390/buildings14092885 - 12 Sep 2024
Viewed by 562
Abstract
Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete [...] Read more.
Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R2) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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15 pages, 8758 KiB  
Article
A Classification Method of Earthquake Ground Motion Records Based on the Results of K-Means Clustering Analysis
by Yanqiong Ding, Minggang Nie, Yazhou Xu and Huiquan Miao
Buildings 2024, 14(6), 1831; https://doi.org/10.3390/buildings14061831 - 16 Jun 2024
Cited by 2 | Viewed by 807
Abstract
This paper presents a classification method for earthquake ground motion records utilizing the results of K-means cluster analysis. The moment magnitude and Joyner–Boore distance are utilized as the primary parameters for clustering the earthquake ground motion records. The classification boundaries are established through [...] Read more.
This paper presents a classification method for earthquake ground motion records utilizing the results of K-means cluster analysis. The moment magnitude and Joyner–Boore distance are utilized as the primary parameters for clustering the earthquake ground motion records. The classification boundaries are established through an examination of moment magnitude ranges, Joyner–Boore distance ranges, and spectral characteristics within each cluster. In this study, a comprehensive dataset comprising 7627 horizontal earthquake acceleration records was meticulously curated for analysis. The data were subjected to separate clustering and grouping procedures, allowing for insightful comparisons between the resultant clusters. Significant disparities in spectral characteristics across the classification groups were demonstrated. These differences become particularly pronounced when a moment magnitude threshold of 6 and a Joyner–Boore distance threshold of 140 km are employed to categorize the ground motion records. The approach underscores the substantial impact of classification based on earthquake ground motion spectral characteristics, while also mitigating the potential instabilities inherent in cluster analysis results. A refined and quantitatively robust framework for understanding and categorizing earthquake ground motions is provided, offering valuable insights for seismic data analysis and contributing to more accurate and reliable assessments of seismic activity. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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18 pages, 2022 KiB  
Article
Influences of the Decomposition Atmosphere and Heating Rate on the Pyrolysis Behaviors of Resin Sand
by Qingwei Xu, Yaping Zhu, Kaili Xu, Bingjun Li and Nan Su
Buildings 2024, 14(5), 1234; https://doi.org/10.3390/buildings14051234 - 26 Apr 2024
Viewed by 977
Abstract
The pouring of sand casting is accompanied by severe heat conduction, and there is an urgent need to investigate the pyrolysis properties of foundry sand. The main purpose of this study was to investigate the pyrolysis behaviors of resin sand, including precoated sand [...] Read more.
The pouring of sand casting is accompanied by severe heat conduction, and there is an urgent need to investigate the pyrolysis properties of foundry sand. The main purpose of this study was to investigate the pyrolysis behaviors of resin sand, including precoated sand (PCS), hot box sand (HBS), and warm box sand (WBS), at heating rates of 20 °C/min, 30 °C/min, and 40 °C/min in nitrogen and air atmospheres. The mass loss of the resin sand was monitored continuously with a simultaneous thermal analyzer, and the kinetic parameters of the resin sand were calculated based on the Coats–Redfern method and thermal data. The average mass loss of the resin sand during pyrolysis was 3.03%, which was much smaller than that of the other sands. The volatile release characteristic index of resin sand could not be calculated based on this concept. To solve this issue, the term Tstv/mloss was established, and its value was determined. With increasing heating rates from 20 °C/min to 30 °C/min and from 30 °C/min to 40 °C/min, the mass losses of the resin sand increased by 0.79% and 0.64%, respectively, and the volatile release characteristic indices of the resin sand increased by 3.8 × 10−10 and 1.06 × 10−9, respectively. In addition, the mass losses and volatile release characteristic indices of resin sand in an air atmosphere were greater than those in a nitrogen atmosphere. With increasing heating rate, the activation energy of the resin sand decreased in a nitrogen atmosphere. The findings concerning the thermal decomposition behaviors of resin sand provided a theoretical basis for the pouring step of the sand casting process. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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28 pages, 10370 KiB  
Article
Bond Strength of Reinforcing Steel Bars in Self-Consolidating Concrete
by Micheal Asaad and George Morcous
Buildings 2023, 13(12), 3009; https://doi.org/10.3390/buildings13123009 - 1 Dec 2023
Cited by 1 | Viewed by 1285
Abstract
This paper presents an experimental investigation of the bond strength of reinforcing steel bars in tension in self-consolidating concrete (SCC). The effects of the reinforcing bar’s location, orientation, size, and coating on the bond strength with SCC were studied and compared to those [...] Read more.
This paper presents an experimental investigation of the bond strength of reinforcing steel bars in tension in self-consolidating concrete (SCC). The effects of the reinforcing bar’s location, orientation, size, and coating on the bond strength with SCC were studied and compared to those with conventionally vibrated concrete (CVC). Several SCC mixtures were developed to cover a wide range of applications/components and material types. The fresh properties of the SCC mixtures were determined to evaluate their filling ability, passing ability and stability. Two hundred and thirty-four pull-out tests of rebars embedded in cubes, wall panels and slabs were conducted. Almost half of the tests were conducted to evaluate the bond with SCC and the other half with CVC. Load–slippage relationships were measured for each test. Pull-out test results were analyzed, and the bond strength was reported in two values: critical strength, which corresponds to slippage of 0.01 in. *0.25 mm); and ultimate strength, which corresponds to the maximum load. The critical strength of SCC and CVC were compared against the ACI 318-19 provisions and comparisons between the ultimate strength of SCC and CVC were conducted. The comparisons indicated that SCC has lower bond strength with vertical rebars than CVC, and a 1.3 development length modification factor is recommended. A similar conclusion applies to epoxy-coated and large diameter rebars. Also, SCC with high slump flow has shown a less top-bar effect than that of CVC. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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Review

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29 pages, 10381 KiB  
Review
Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research
by Yi Hu, Wentao Wang, Lei Li and Fangjun Wang
Buildings 2024, 14(5), 1393; https://doi.org/10.3390/buildings14051393 - 13 May 2024
Cited by 1 | Viewed by 2566
Abstract
Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting [...] Read more.
Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods. Full article
(This article belongs to the Special Issue Emerging Techniques in Concrete Materials and Structures: Experiments, Theories and Applications)
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