Automatic concrete health monitoring: assessment and monitoring of concrete surfaces

Carmo

2019

Structural Concrete

Self Cite

The models to predict the shear behavior in reinforced concrete (RC) members are still far from be consensual. Thus, a detailed understanding of the physical phenomenon and a detailed monitoring of shear tests on RC members is fundamental. Identify and quantify separately the contribution of each component of the shear transfer mechanism it is a challenge. The traditional instrumentation usually results in the evaluation of a limited number of sections. Additionally, technologies based on image analysis have been applied to structural monitoring with excellent results. In this paper, the method Photo‐Shear, developed to monitoring shear tests based on photogrammetry, is presented. The method allows a better understanding of the shear strength mechanism by acquiring data from the surface of RC members. Then, postprocessing procedures leads to outputs with the redundancy and complementarity necessary to characterize: (a) displacement and strain fields; (b) overall shear deformation (distortion and rotation); (c) load path and direction of the compression strut; (d) shear cracks' characterization; and (e) the aggregate interlock effect. The method was validated by monitoring two RC beams tests. Based on the results obtained, several advantages of the proposed Photo‐Shear method were demonstrated over the traditional instrumentation, namely, the quantity and quality of the information obtained, and the redundancy and complementarity of the results analyzed.

Section: Image Analysismentioning

confidence: 99%

Evaluation of the shear transfer mechanisms in reinforced concrete beams using photogrammetry

Carmo

2019

Structural Concrete

Self Cite

“…Reliable solutions are provided because a large amount of high‐quality information is achieved. In this context, methods based on photogrammetry and image processing have been developed and applied to monitoring of RC structures . In addition, commercial packages of software and hardware are available allowing nonspecialists to apply photogrammetry in their industrial and research works.…”

Section: Introductionmentioning

confidence: 99%

A new method for corrosion assessment of reinforcing bars based on close‐range photogrammetry: Experimental validation

Cavaco

Pimenta

2019

Structural Concrete

Self Cite

In most developed countries, a significant portion of the nation's Gross Domestic Product (GDP) is spent on maintenance of reinforced concrete (RC) structures suffering from corrosion. Although extensive research has been devoted to this issue, little attention has been given to the study of accurate methods to quantify corrosion and its spatial variability. As a result, scattered data can be found in the literature, relating the rates of corrosion, its penetration depth, and the resulting structural deteriorating effects. In this paper, a new cost‐effective and versatile method for corrosion assessment of RC structures based on close‐range photogrammetry is proposed. This method was validated by applying it to an experimental research on an accelerated corrosion test of reinforcing bars (RBs). The tests performed show that the proposed method can provide accurate results independent of the RB diameter, length, and corrosion level (measured in terms of rebar's cross section loss percentage), as long as sufficient image overlapping is ensured along the axial and radial axes of the rebar. The results obtained also prove that the method is suitable to measure spatial variability of corrosion attack, providing much better results than those gained from existing geometrical models based on the maximum pit depth. More precise results can be achieved by using semi‐professional Digital Single‐Lens Reflex Cameras (DSLR) cameras having the additional advantage to be suitable for low levels of corrosion attack. However, the research performed shows that accurate results can also be obtained even when a less sophisticated or a smartphone camera is used.

“…The new technologies can be very helpful to achieve this goal. In the last decade, methods based on computer vision have been presented, attesting to be a reliable solution that can provide a large amount of information with high quality . In this context, several methods based on photogrammetry and image processing have been developed and applied in monitoring of RC members.…”

Section: Introductionmentioning

confidence: 99%

Method for assessing beam column joints in RC structures using photogrammetric computer vision

Struct Control Health Monit

Carmo

2017

Summary The response of beam column joints is fundamental for structural analysis of reinforcement concrete frames, particularly, when subjected to dynamic actions. Monitoring the key parameters of those joints is essential to perform a detailed analysis of the structure. These are often difficult to obtain, and only a few sections in critical regions are assessed. High‐end technologies can bring huge advantages in such measurements. Methods based on computer vision have been applied to monitoring reinforced concrete structures with excellent results, considering the accuracy achieved and the large quantity of information recorded. In this paper, a new method, named “Photo‐Node,” based on photogrammetry and image processing is presented. Photo‐Node is a noncontact method used to directly monitoring concrete surfaces, developed to measure the relevant physical parameters of reinforcement concrete beam column joints during experimental tests. The method addresses several postprocessing features to evaluate joint rotation, joint distortion, principal directions and stresses, cracks pattern, and load path. Additionally, the critical sections of the beam, that is, the region adjacent to the joint where the formation of the plastic hinge is expected, can be also evaluated. Photo‐Node provides a large amount of data, which can be processed to properly assess the joints behaviour. The degree of discretisation achieved enables to identify behaviours impossible to detect using traditional and less discretised approaches. The application of the method showed promising and accurate results, proving to be an efficient and a wide‐ranging tool to be used in experimental programs.