Corrosion effects on the capacity and ductility of concrete half-joint bridges

Rosso, Marco Martino; Asso, Rebecca; Aloisio, Angelo; Benedetto, Marilisa Di; Cucuzza, Raffaele; Greco, Rita

doi:10.1016/j.conbuildmat.2022.129555

Cited by 16 publications

(19 citation statements)

References 44 publications

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“…The studies extended even to structural health monitoring, corrosion in steel rebars, durability, etc. (Rosso et al, 2022).…”

Section: Bibliometric Analysis Of Machine Learning Applications In Co...mentioning

confidence: 99%

“…The technique showed good feasibility and adequate reliability to predict corrosion. Another recent finding (Rosso et al, 2022), used non-linear analysis to model the corrosion and ductility of concrete used in halfjoint bridges, the finding showed discussed two case studies behavior which possessed different behavior whereby the first showed a significant reduction of capacity and ductility while the second didn't behave similarly, the study managed to model the corrosion of the joints. In a different approach (Di Trapani et al, 2020), used a generic algorithms-based framework to optimize the jacking retrofitting in reinforced concrete columns and the findings showed the framework enabled the effecting modeling of retrofitting costs and can be used by practitioners as a tool for retrofitting modeling.…”

Section: Figurementioning

confidence: 99%

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Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy.

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“…The studies extended even to structural health monitoring, corrosion in steel rebars, durability, etc. (Rosso et al, 2022).…”

Section: Bibliometric Analysis Of Machine Learning Applications In Co...mentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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“…More recently, some NLFE results of degraded dapped-end beams have been presented. [35][36][37][38][39][40][41] In particular, Santarsiero et al 35 proposed a 3D NLFE model for a dapped-end with variable thickness where nonlinear truss elements were embedded in concrete. Reinforcement corrosion effects were simulated by reducing only the transversal cross-section of corroded rebars due to chloride ingress over time to predict the residual ultimate capacity.…”

Section: Introductionmentioning

confidence: 99%

“…In the above-mentioned works, the NLFE models were validated with respect to experimental test on uncorroded specimens and then they were used to investigate the effects of corrosion on the structural response through sensitivity analyses at varying levels of corrosion, [35][36][37][38][39][40][41] without experimental validation. In general, for all models of corroded dapped-end beams, the problem of their validation is an open issue, both because of the uncertainties of the corrosion parameters to be used as inputs 42 and because of a lack of experimental tests on corroded dapped-end beams.…”

Section: Introductionmentioning

confidence: 99%

Capacity assessment of uncorroded and corroded dapped‐end beams by NLFE and strut‐and‐tie based methods

Belletti,

Calcavecchia,

Ferretti

et al. 2024

Structural Concrete

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The verification of dapped‐end beams degraded by corrosion is a problem, especially for existing bridges in service. This paper proposes a nonlinear finite element (NLFE) modeling procedure and a simple strut‐and‐tie based procedure for predicting the response of dapped‐end beams subjected to chloride corrosion. Firstly, the finite element modeling strategy, based on the adoption of multilayer shell elements and the PARC_CL 2.1 crack model is described. Then, the degradation effects on concrete, rebars, and steel‐to‐concrete interaction are defined as a function of the propagation period of corrosion. In particular, the effects of corrosion on the reinforcement are modeled by applying a reduction of tensile strength that considers for both the reduction of cross‐section and the ultimate strain caused by pitting. Concrete splitting cracking due to volume expansion during rust formation is modeled by reducing the mechanical properties of concrete. Corrosion effects in steel‐to‐concrete interaction are modeled by applying a bond strength decay to the spring elements connecting corroded rebars—modeled with truss elements—and concrete multilayer shell elements. The proposed finite element procedure is used to study two scenarios based on different spatial distributions of corrosion‐prone areas. Subsequently, a simplified analytical approach based on the strut‐and‐corroded tie method—called S&CT method—is proposed and compared with the finite element outcomes. Finally, the validations of the two proposed methods are presented with respect to a corroded dapped‐end beam, showing that corrosion of rebars affects the resistance mechanisms of the dapped‐end beam, by reducing both resistance and ductility. The proposed simplified analytical S&CT method provides conservative and safe results compared to the numerical NLFE model and to experimental data.

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“…The need to preserve and maintain historical buildings, structural heritage and civil infrastructures combined with improved safety standards has led to the increasing use of structural health monitoring (SHM) techniques [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The adoption of reliable and rigorous monitoring systems is fundamental to reducing maintenance costs and at the same time extending the service life of the existing structures.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Melchiorre

Bertetto

Rosso

et al. 2023

Sensors

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The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

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Corrosion effects on the capacity and ductility of concrete half-joint bridges

Cited by 16 publications

References 44 publications

Machine learning in concrete technology: A review of current researches, trends, and applications

Machine learning in concrete technology: A review of current researches, trends, and applications

Capacity assessment of uncorroded and corroded dapped‐end beams by NLFE and strut‐and‐tie based methods

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

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