Numerical modeling approach taking into account the influence of delamination for performance capacity of reinforced concrete beam strengthened in bending by CFRP

In this paper, an intelligent modeling approach is presented to predict the shear strength of the internal reinforced concrete (RC) beam-column joints and used to analyze the sensitivity of the influence factors on the shear strength. The proposed approach is established based on the famous boosting-family ensemble machine learning (ML) algorithms, i.e., gradient boosting regression tree (GBRT), which generates a strong predictive model by integrating several weak predictors, which are obtained by the well-known individual ML algorithms, e.g., DT, ANN, and SVM. The strong model is boosted as each weak predictor has its own weight in the final combination according to the performance. Compared with the conventional mechanical-driven shear strength models, e.g., the well-known modified compression field theory (MCFT), the proposed model can avoid the complicated derivation process of shear mechanism and calibration of the involved empirical parameters; thus, it provides a more convenient, fast, and robust alternative way for predicting the shear strength of the internal RC joints. To train and test the GBRT model, a total of 86 internal RC joint specimens are collected from the literatures, and four traditional ML models and the MCFT model are also employed as comparisons. The results indicate that the GBRT model is superior to both the traditional ML models and MCFT model, as its degree-of-fitting is the highest and the predicting dispersion is the lowest. Finally, the model is used to investigate the influences of different parameters on the shear strength of the internal RC joint, and the sensitivity and importance of the corresponding parameters are obtained.

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Behavior of Heated Damaged Reinforced Concrete Beam-Column Joints Strengthened with FRP

Case Studies in Construction Materials ◽

10.1016/j.cscm.2021.e00584 ◽

2021 ◽

pp. e00584

Author(s):

Rajai Z. Al-Rousan ◽

Ayah Alkhawaldeh

Keyword(s):

Reinforced Concrete ◽

Concrete Beam ◽

Reinforced Concrete Beam

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Evaluation of Severely Damaged Reinforced Concrete Beam Repaired with Epoxy Injection using Acoustic Emission Technique

Theoretical and Applied Fracture Mechanics ◽

10.1016/j.tafmec.2020.102890 ◽

2021 ◽

pp. 102890

Author(s):

Soffian Noor Mat Saliah ◽

Noorsuhada Md Nor ◽

Noorhazlinda Abd Rahman ◽

Shahrum Abdullah ◽

Mohd Subri Tahir

Keyword(s):

Acoustic Emission ◽

Reinforced Concrete ◽

Concrete Beam ◽

Reinforced Concrete Beam ◽

Acoustic Emission Technique

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Toward Structural Health Monitoring of Civil Structures Based on Self-Sensing Concrete Nanocomposites: A Validation in a Reinforced-Concrete Beam

International Journal of Concrete Structures and Materials ◽

10.1186/s40069-020-00451-8 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

Diego L. Castañeda-Saldarriaga ◽

Joham Alvarez-Montoya ◽

Vladimir Martínez-Tejada ◽

Julián Sierra-Pérez

Keyword(s):

Reinforced Concrete ◽

Damage Detection ◽

Direct Current ◽

Health Monitoring ◽

Electrical Resistance ◽

Concrete Beam ◽

Reinforced Concrete Beam ◽

Structural Member ◽

Rc Beam ◽

Electric Resistance

AbstractSelf-sensing concrete materials, also known as smart concretes, are emerging as a promising technological development for the construction industry, where novel materials with the capability of providing information about the structural integrity while operating as a structural material are required. Despite progress in the field, there are issues related to the integration of these composites in full-scale structural members that need to be addressed before broad practical implementations. This article reports the manufacturing and multipurpose experimental characterization of a cement-based matrix (CBM) composite with carbon nanotube (CNT) inclusions and its integration inside a representative structural member. Methodologies based on current–voltage (I–V) curves, direct current (DC), and biphasic direct current (BDC) were used to study and characterize the electric resistance of the CNT/CBM composite. Their self-sensing behavior was studied using a compression test, while electric resistance measures were taken. To evaluate the damage detection capability, a CNT/CBM parallelepiped was embedded into a reinforced-concrete beam (RC beam) and tested under three-point bending. Principal finding includes the validation of the material’s piezoresistivity behavior and its suitability to be used as strain sensor. Also, test results showed that manufactured composites exhibit an Ohmic response. The embedded CNT/CBM material exhibited a dominant linear proportionality between electrical resistance values, load magnitude, and strain changes into the RC beam. Finally, a change in the global stiffness (associated with a damage occurrence on the beam) was successfully self-sensed using the manufactured sensor by means of the variation in the electrical resistance. These results demonstrate the potential of CNT/CBM composites to be used in real-world structural health monitoring (SHM) applications for damage detection by identifying changes in stiffness of the monitored structural member.

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