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Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures
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Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 5369

Special Issue Editors

Dr. Quanwang Li

E-Mail Website
Guest Editor
School of Civil Engineering, Tsinghua University, Beijing, China
Interests: seismic performance and safety analysis of structure; evaluation of bearing capacity; reliability analysis; maintenance management of existing structures; durability evaluation and design of concrete structures; structural degradation model and time-varying reliability
Dr. Zeyu Wang

E-Mail Website
Guest Editor
School of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: machine learning; structural reliability; uncertainty quantification; Bayesian updating; surrogate modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to present the latest research in the field of RC deterioration modeling, assessment, and control through maintenance techniques/plans. The Special Issue will cover the models of RC deterioration processes (physical-law-based or measured-data-based), model-updating methods using inspection/monitoring data, durability assessment methods of RC structures in actual service environments, and maintenance planning to achieve the specified service life. In addition to the requirements around contributions to real-world structural deterioration problems, this Special Issue will strongly emphasize the novelty and applicability of the developed deterioration models and assessment methods, and in-depth comparisons with in situ data. Examples of topics of interest include but are not limited to the following:

  • Models of RC deterioration processes in complex in-service environments
  • Deterioration model updates using inspection/monitoring data
  • Reliability methods developed for durability assessment of deteriorating RC structures
  • Optimization of RC structure maintenance based on life-cycle performance target and cost
  • Deterioration modeling of RC structures after maintenance/repair
  • Quantitative durability design of RC structures for specified service lives

Dr. Quanwang Li
Dr. Zeyu 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. Materials 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

  • RC deterioration modeling
  • RC structures’ durability assessment and maintenance
  • lifetime evaluation
  • quantitative design
  • precast concrete
  • inspection of deteriorating structures

Published Papers (4 papers)

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Research

24 pages, 8228 KiB  
Article
Numerical Simulation of the Effect of Freeze-Thaw Cycles on the Axial Compression Strength of Rubber Concrete
by Dingyi Hao, Xiaoyu Huang, Houmin Li, Zhou Cao, Zijiang Yang, Xianfeng Pei, Kai Min, Cai Liu, Wenchao Li, En Zhang and Jie Shen
Materials 2023, 16(12), 4460; https://doi.org/10.3390/ma16124460 - 19 Jun 2023
Cited by 1 | Viewed by 775
Abstract
The incorporation of rubber can enhance concrete’s durability and effectively reduce the damage caused by freeze-thaw cycling (FTC). Still, there has been only limited research on the damage mechanism of RC at the fine view level. To gain insight into the expansion process [...] Read more.
The incorporation of rubber can enhance concrete’s durability and effectively reduce the damage caused by freeze-thaw cycling (FTC). Still, there has been only limited research on the damage mechanism of RC at the fine view level. To gain insight into the expansion process of uniaxial compression damage cracks in rubber concrete (RC) and summarize the internal temperature field distribution law during FTC, a fine RC thermodynamic model containing mortar, aggregate, rubber, water, and interfacial transition zone (ITZ) is established in this paper, and the cohesive element is selected for the ITZ part. The model can be used to study the mechanical properties of concrete before and after FTC. The validity of the calculation method was verified by comparing the calculated results of the compressive strength of concrete before and after FTC with the experimental results. On this basis, this study analyzed the compressive crack extension and internal temperature distribution of RC at 0, 5, 10, and 15% replacement rates before and after 0, 50, 100, and 150 cycles of FTC. The results showed that the fine-scale numerical simulation method can effectively reflect the mechanical properties of RC before and after FTC, and the computational results verify the applicability of the method to rubber concrete. The model can effectively reflect the uniaxial compression cracking pattern of RC before and after FTC. Incorporating rubber can impede temperature transfer and reduce the compressive strength loss caused by FTC in concrete. The FTC damage to RC can be reduced to a greater extent when the rubber incorporation is 10%. Full article
(This article belongs to the Special Issue Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures)
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20 pages, 6112 KiB  
Article
Empirical Study of Surface Deterioration Analysis Based on Random Fields for Reinforced Concrete Structures in Marine Environment
by Guixiang Yi, Xinyi Ye and Quanwang Li
Materials 2023, 16(11), 4150; https://doi.org/10.3390/ma16114150 - 02 Jun 2023
Cited by 2 | Viewed by 920
Abstract
Corrosion-induced deterioration of the in-service marine reinforced concrete (RC) structures may result in unsatisfactory serviceability or insufficient safety. Surface deterioration analysis based on random fields can provide information regarding the future development of the surface damage of the in-service RC members, but its [...] Read more.
Corrosion-induced deterioration of the in-service marine reinforced concrete (RC) structures may result in unsatisfactory serviceability or insufficient safety. Surface deterioration analysis based on random fields can provide information regarding the future development of the surface damage of the in-service RC members, but its accuracy needs to be verified in order to broaden its applications in durability assessment. This paper performs an empirical study to verify the accuracy of the surface deterioration analysis based on random fields. The batch-casting effect is considered to establish the “step-shaped” random fields for stochastic parameters in order to better coordinate their actual spatial distributions. Inspection data from a 23-year-old high-pile wharf is obtained and analyzed in this study. The simulation results of the RC panel members’ surface deterioration are compared with the in-situ inspection results with respect to the steel cross-section loss, cracking proportion, maximum crack width, and surface damage grades. It shows that the simulation results coordinate well with the inspection results. On this basis, four maintenance options are established and compared in terms of the total amounts of RC panel members needing restoration and the total economic costs. It provides a comparative tool to aid the owners in selecting the optimal maintenance action given the inspection results, to minimize the lifecycle cost and guarantee the sufficient serviceability and safety of the structures. Full article
(This article belongs to the Special Issue Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures)
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21 pages, 7437 KiB  
Article
Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams
by Peixi Yang, Chuanqi Li, Yingui Qiu, Shuai Huang and Jian Zhou
Materials 2023, 16(11), 4034; https://doi.org/10.3390/ma16114034 - 28 May 2023
Cited by 4 | Viewed by 1315
Abstract
Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer [...] Read more.
Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab’s effective depth (SED), span–depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab’s effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control. Full article
(This article belongs to the Special Issue Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures)
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29 pages, 7845 KiB  
Article
Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete
by Magdalena Rajczakowska, Maciej Szeląg, Karin Habermehl-Cwirzen, Hans Hedlund and Andrzej Cwirzen
Materials 2023, 16(3), 1273; https://doi.org/10.3390/ma16031273 - 02 Feb 2023
Cited by 7 | Viewed by 1562
Abstract
Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to [...] Read more.
Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors. Full article
(This article belongs to the Special Issue Modelling, Assessing and Controlling Deterioration Process of Reinforced Concrete Structures)
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