Prediction of the degree of steel corrosion damage in reinforced concrete using field-based data by multi-gene genetic programming approach

2022 ◽  
Author(s):  
Zahra Rajabi ◽  
Mahdi Eftekhari ◽  
Mohammad Ghorbani ◽  
Maryam Ehteshamzadeh ◽  
Hadi Beirami
Keyword(s):  
Reinforced Concrete ◽  
Genetic Programming ◽  
Steel Corrosion ◽  
Corrosion Damage ◽  
Programming Approach

scholarly journals Towards arresting reinforced concrete corrosion – a review

2018 ◽  
Vol 199 ◽  
pp. 05001
Author(s):  
Christian Christodoulou ◽  
Chris Goodier ◽  
Gareth Glass
Keyword(s):  
Reinforced Concrete ◽  
Solid Phase ◽  
Steel Corrosion ◽  
Corrosion Damage ◽  
Sacrificial Anodes ◽  
Corrosion Initiation ◽  
Chloride Induced Corrosion ◽  
Corrosion Risk ◽  
Corrosion Of Steel ◽  
Reinforced Concrete Corrosion

This work reviews developments in the understanding of chloride induced corrosion of steel in concrete from both a kinetic and thermodynamic perspective. Corrosion damage is at least in part attributed to the production of acid at sites of corrosion initiation. Solid phase inhibitors provide a reservoir of hydroxyl ions to inhibit damage. Pit re-alkalisation is identified as an important protective effect in electrochemical treatments used to arrest corrosion. A process like pit re-alkalisation is achieved more easily by impressing current from sacrificial anodes using a power supply which may then be followed by low maintenance galvanic protection to prevent local acidification. Methods for monitoring the steel corrosion rate in electrochemically treated reinforced concrete have been developed and used to assess corrosion risk. Some of these concepts have been adopted in the recent international standard on cathodic protection, ISO 12696:2016, some of the amendments of which are considered in the work presented here.

scholarly journals Degradation Monitoring in Reinforced Concrete with 3D Localization of Rebar Corrosion and Related Concrete Cracking

2021 ◽  
Vol 11 (15) ◽  
pp. 6772
Author(s):  
Charlotte Van Steen ◽  
Els Verstrynge
Keyword(s):  
Reinforced Concrete ◽  
Degradation Mechanism ◽  
Heterogeneous Material ◽  
Corrosion Damage ◽  
Corrosion Process ◽  
Internal Damage ◽  
Rc Structures ◽  
Degradation Monitoring ◽  
Rebar Corrosion ◽  
Width Measurements

Corrosion of the reinforcement is a major degradation mechanism affecting durability and safety of reinforced concrete (RC) structures. As the corrosion process starts internally, it can take years before visual damage can be noticed on the surface, resulting in an overall degraded condition and leading to large financial costs for maintenance and repair. The acoustic emission (AE) technique enables the continuous monitoring of the progress of internal cracking in a non-invasive way. However, as RC is a heterogeneous material, reliable damage detection and localization remains challenging. This paper presents extensive experimental research aiming at localizing internal damage in RC during the corrosion process. Results of corrosion damage monitoring with AE are presented and validated on three sample scales: small mortar samples (scale 1), RC prisms (scale 2), and RC beams (scale 3). For each scale, the corrosion process was accelerated by imposing a direct current. It is found that the AE technique can detect damage earlier than visual inspection. However, dedicated filtering is necessary to reliably localize AE events. Therefore, AE signals were filtered by a newly developed post-processing protocol which significantly improves the localization results. On the smallest scale, results were confirmed with 3D micro-CT imaging, whereas on scales 2 and 3, results were compared with surface crack width measurements and resulting rebar corrosion levels.

scholarly journals Relationship model between surface strain of concrete and expansion force of reinforcement rust

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fanxiu Chen ◽  
Zuquan Jin ◽  
Endong Wang ◽  
Lanqin Wang ◽  
Yudan Jiang ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Real Time ◽  
Steel Corrosion ◽  
Surface Strain ◽  
Concrete Cracking ◽  
Whole Process ◽  
Steel Bars ◽  
Relationship Model ◽  
Expansion Force ◽  
Corrosion Of Steel

AbstractConcrete cracking caused by corrosion of reinforcement could significantly shorten the durability of reinforced concrete structure. It remains critical to investigate the process and mechanism of the corrosion occurring to concrete reinforcement and establish the theoretical prediction model of concrete expansion force for the whole process of corrosion cracking of reinforcement. Under the premise of uniform corrosion of reinforcing steel bars, the elastic mechanics analysis method is adopted to analyze the entire process starting from the corrosion of steel bars to the cracking of concrete due to corrosion. A relationship model between the expansion force of corrosion of steel bars and the surface strain of concrete is established. On the cuboid reinforced concrete specimens with square cross-sections, accelerated corrosion tests are carried out to calibrate and verify the established model. The model can be able to estimate the real-time expansion force of reinforced concrete at any time of the whole process from the initiation of steel corrosion to the end of concrete cracking by measuring the surface strain of concrete. It could be useful for quantitative real-time monitoring of steel corrosion in concrete structures.

Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming

2020 ◽  
Vol 37 (7) ◽  
pp. 2517-2537
Author(s):  
Mostafa Rezvani Sharif ◽  
Seyed Mohammad Reza Sadri Tabaei Zavareh
Keyword(s):  
Numerical Modeling ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Numerical Analysis ◽  
Genetic Programming ◽  
Linear Genetic Programming ◽  
Rc Columns ◽  
Content Type ◽  
Alternative Approach ◽  
Engineering Problems

Purpose The shear strength of reinforced concrete (RC) columns under cyclic lateral loading is a crucial concern, particularly, in the seismic design of RC structures. Considering the costly procedure of testing methods for measuring the real value of the shear strength factor and the existence of several parameters impacting the system behavior, numerical modeling techniques have been very much appreciated by engineers and researchers. This study aims to propose a new model for estimation of the shear strength of cyclically loaded circular RC columns through a robust computational intelligence approach, namely, linear genetic programming (LGP). Design/methodology/approach LGP is a data-driven self-adaptive algorithm recently used for classification, pattern recognition and numerical modeling of engineering problems. A reliable database consisting of 64 experimental data is collected for the development of shear strength LGP models here. The obtained models are evaluated from both engineering and accuracy perspectives by means of several indicators and supplementary studies and the optimal model is presented for further purposes. Additionally, the capability of LGP is examined to be used as an alternative approach for the numerical analysis of engineering problems. Findings A new predictive model is proposed for the estimation of the shear strength of cyclically loaded circular RC columns using the LGP approach. To demonstrate the capability of the proposed model, the analysis results are compared to those obtained by some well-known models recommended in the existing literature. The results confirm the potential of the LGP approach for numerical analysis of engineering problems in addition to the fact that the obtained LGP model outperforms existing models in estimation and predictability. Originality/value This paper mainly represents the capability of the LGP approach as a robust alternative approach among existing analytical and numerical methods for modeling and analysis of relevant engineering approximation and estimation problems. The authors are confident that the shear strength model proposed can be used for design and pre-design aims. The authors also declare that they have no conflict of interest.

Polypropylene Fibre Reinforced Concrete Air Permeability

1990 ◽  
Vol 211 ◽  
Author(s):  
Miguel A. Sanjuan ◽  
A. Moragues ◽  
B. Bacle ◽  
C. Andrade
Keyword(s):  
Carbon Dioxide ◽  
Reinforced Concrete ◽  
Steel Corrosion ◽  
Air Permeability ◽  
Polypropylene Fibre ◽  
Fibre Reinforced Concrete ◽  
Polypropylene Fibres ◽  
Cement Ratio ◽  
Reinforcing Steel Corrosion

AbstractThe permeability of concrete to gases is of direct importance to the durability of concrete structures, because of carbon dioxide flowing through the concrete favour lime carbonation and reinforcing steel corrosion.Mortar with and without polypropylene fibres having water/cementitious ratios of 0.30, 0.35 and 0.40 and a cement/sand ratio of 1/1 were studied. Polypropylene dosage varied from 0.1 to 0.3% by volume of cement.The characterization of mortar permeability was made using cylindrical shaped samples (3 cm height and 15 cm diameter). These specimens were 28 days cured and then dried before the test.The addition of fibres results in a decrease of air permeability. Variation of the water/cement ratio is of lesser importance than fiber addition.

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

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