NON-DESTRUCTIVE TESTING OF CONCRETE

SUSTAINABLE PROACTIVE ADVOCACIES IN CIVIL ENGINEERING 2018 (SPACE 2018) “CIVIL ENGINEERS AND THE BUILT ENVIRONMENT: A VISION FOR SUSTAINABLE DEVELOPMENT”

August 9-10, 2018, Century Park Hotel, Manila

NON-DESTRUCTIVE TESTING OF CONCRETE STRUCTURES by Jason Maximino C. Ongpeng, D.Eng. Assistant Professor, Department of Civil Engineering Department, DLSU- Manila President, CTMex Engineering Consultancy President, Ongpeng-Santos Construction Corporation

Abstract : This paper discusses non-destructive testing (NDT) in concrete structures using applied test like rebound hammer and ultrasonic pulse velocity (UPV) test. In addition, advancement and researches in concrete structures on nonlinear ultrasonic test, acoustic emission test, digital image processing, and aerial drone technology will be tackled. Lastly, an application of artificial intelligence (AI) with the available experimental data from controlled experiments will be introduced.

Keywords: Non-destructive, Nonlinear ultrasonic, Acoustic emission, Digital image, Drone, Artificial intelligence

1

INTRODUCTION

Structural health monitoring is very important in today’s modern world. Infrastructures experiences man-made and natural disasters that needs more precise and accurate assessment during post event. Material that is commonly used in structures in concrete. Concrete can be assessed in many ways where factors such as cost, time, and the idle period during assessment should be considered.

some cases, combination of these two methods (SONREB) considered to be more reliable and accurate. Table 1. Factors influencing rebound hammer (Breysse 2012)

One way of having a better and economical test is the use of non-destructive test (NDT). However, NDT in concrete is very complex since it comprises of inhomogeneous mixture. Cement paste, fine and coarse aggregates, and interfacial transition zone (ITZ) bring complex material behavior during a test. Figure 1 shows the comparison of how we do test in human versus structure. Table 1 shows the factors that influences rebound hammer. Shown in Figure 2 is the time to travel of the elastic wave from the transmitter to receiver. The UPV is solved as quotient of the distance between transducers and the time to travel.

Fig. 1 Comparable treatment between ningen-dock and infra-dock (Ohtsu 2018)

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NON-DESTRUCTIVE TEST

2.1 Rebound Hammer and Ultrasonic Pulse Velocity Test In practice, assessing concrete structures include the use of rebound hammer and or ultrasonic pulse velocity (UPV). In

Fig. 2 Ultrasonic Time Domain Waveform (Ongpeng et. al 2017)

SUSTAINABLE PROACTIVE ADVOCACIES IN CIVIL ENGINEERING 2018 (SPACE 2018) “CIVIL ENGINEERS AND THE BUILT ENVIRONMENT: A VISION FOR SUSTAINABLE DEVELOPMENT”

August 9-10, 2018, Century Park Hotel, Manila However, this practice and with the available test standards for it was proven to be less sensitive to micro-crack damages. 2.2 Nonlinear Ultrasonic Test and Acoustic Emission (AE) Test Another way of testing concrete structure is the use of test that is sensitive to micro-crack damages. An example of this is the use of nonlinear ultrasonic test and or acoustic emission test.

Fig. 5 Convex hull volume formation from AESL in unreinforced concrete cubes (Ongpeng et. al 2016)

Fig. 3 Nonlinear ultrasonic test using higher harmonic generation (Ongpeng et. al 2016)

Fig. 6 Convex hull volume formation from AESL in reinforced concrete beams (Ongpeng et. al 2018) 2.3 Digital Image Correlation and Aerial Drone Technology

Fig. 4 Normalized 2nd harmonic frequency generation VS load with different sizes of aggregates from smallest (S1) to largest (S4) (Ongpeng et. al 2017) The nonlinear ultrasonic test uses higher harmonic generation as shown in Figure 3. It can be seen that the normalized 2 nd harmonic frequency generation was sensitive to the changes in load for small and large aggregates in unreinforced concrete cubes shown in Figure 4.

Common problems in infrastructure assessment is to identify which structures should be prioritized before NDT or any tests are made. In order to decide on which structures should be considered, noncontact NDT is applicable to investigate and assess structures. Examples of noncontact NDT is the use of Digital Image Correlation (DIC) shown in Figure 7 and or Drone technology.

In addition, acoustic emission test produces signals from sensors. Specifically, acoustic emission source location (AESL) was used to look into the behavior of the sounds inside the concrete. In visualizing how the AESL behaves with respect to load, convex hull volume algorithm was used. Shown in Figure 5 and Figure 6 are examples of how convex hull volume formation behaves with load for an unreinforced concrete cube and reinforced concrete beams, respectively. Fig. 7 Use of DIC in detecting corrosion and cracks (Xianyu et. al 2018)

SUSTAINABLE PROACTIVE ADVOCACIES IN CIVIL ENGINEERING 2018 (SPACE 2018) “CIVIL ENGINEERS AND THE BUILT ENVIRONMENT: A VISION FOR SUSTAINABLE DEVELOPMENT”

August 9-10, 2018, Century Park Hotel, Manila behavior of parameters with respect to an output parameter. An example of AI simulation is shown in Figure 11 where the variation of input parameters was made to show how the compressive strength behave. In addition, Table 2 shows the sensitivity output of neural network simulation applied to noisy datasets taken from reinforced concrete beams.

Fig. 10 Neural network (Ongpeng et. al 2017) Fig. 8 Historical development of drone (Rakha et. al 2018)

Fig. 11 Simulation using AI (Ongpeng et. al 2017) Table 2. Analysis of sensitivity using AI simulation (Ongpeng et. al 2018)

Fig. 9 Sample on the use of drone (Seo et. al 2018) Figure 8 shows the development of drone technology over the past decades and how it was used. The drone specifically for structural assessment can be used using digital image camera to identify defects such as spalling, moisture, efflorescence and stains as shown in Figure 9.

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USE OF ARTIFICIAL INTELLIGENCE IN NDT

In research, arriving at a conclusion from various noises in the available numerous datasets are mostly experienced. In order to arrive at conclusive remarks, artificial intelligence such as neural network can be used. Shown in Figure 10 is an example of a feed-forward back propagation neural network with 4 inputs and 1 output. This network architecture once proven to be reliable can be used for simulations in determining

4

CONCLUSION

Non-destructive test in concrete structures have been an interesting topic in applied practice and in research. Different tests resulted to different sensitivity against internal damage. Additional technology such as drone and artificial intelligence were seen as a good potential to supplement NDT test.

SUSTAINABLE PROACTIVE ADVOCACIES IN CIVIL ENGINEERING 2018 (SPACE 2018) “CIVIL ENGINEERS AND THE BUILT ENVIRONMENT: A VISION FOR SUSTAINABLE DEVELOPMENT”

August 9-10, 2018, Century Park Hotel, Manila ABOUT THE AUTHOR Jason Maximino C. Ongpeng was a JSPS Ronpaku Scholar from year 2014 to 2017 in Tokyo Institute of Technology. He is an assistant professor in the Civil Engineering Department of DLSU- Manila. He is also active in consultancy works and the construction industry. REFERENCES Breysse, D. (2012). Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods. Construction and Building Materials, 33, 139-163 Breysse, D. and Balayssac, J.P. (2018). Non-destructive testing and assessment of reinforced concrete and masonry structures. Construction and Building Materials, 182, 1-9 Lacidogna, G., Piana, G. and Carpinteri, A. (2018). Damage monitoring of three-point bending concrete specimens by acoustic emission and natural frequency analysis. Engineering Fracture Mechanics, IN PRESS. https://doi.org/10.1016/j.engfracmech.2018.06.034. Ohtsu, M. (2018). Prospective applications of AE measurements to infra-dock of concrete structures. Construction and Building Materials, 158, 1134-1142 Ongpeng, J., Oreta, A. and Hirose, S. (2018). Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams using Neural Network. Applied Science MDPI Switzerland, 8(3), 405 Ongpeng, J., Oreta, A. and Hirose, S. (2018). Monitoring Damage Using Acoustic Emission Source Location and Computational Geometry in Reinforced Concrete Beams. Applied Science MDPI Switzerland, 8(2), 189 Ongpeng, J. (2017). Ultrasonic Pulse Velocity Test of Reinforced Concrete with Induced Corrosion. ASEAN Engineering Journal Part C, 7(2), 9-17 Ongpeng, J., Oreta, A., Hirose, S. and Nakahata, K. (2017). Nonlinear Ultrasonic Investigation of Concrete with Varying Aggregate Size under Uniaxial Compression Loading and Unloading. Journal of Materials in Civil Engineering, 29(2). doi: 10.1061/(ASCE)MT.19435533.0001726 Ongpeng, J., Oreta, A., Soberano, M. and Hirose, S. (2017). Artificial Neural Network Model using Ultrasonic Test Results to Predict Compressive Stress in Concrete. Computers and Concrete, 19(1): 59-68 Ongpeng, J., Oreta, A. and Hirose, S. (2016). Damage Progression in Concrete using Acoustic Emission Test through Convex Hull Visualization. ACI Materials, 113 (6): 737-744 Ongpeng, J., Oreta, A. and Hirose, S. (2016). Effect of Load Pattern in the Generation of Higher Harmonic Amplitude in Concrete Using Nonlinear Ultrasonic Test. Journal of Advanced Concrete Technology, 14 (5): 205-214 Rakha, T. and Gorodetsky, A. (2018). Review on Unmanned Aerial System (UAS) applications in the built environment:

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