• Computers and Concrete
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• v.26 no.6
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• pp.533-546
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• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.342-347
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• 2004

The dynamic responses of a PSC bridge for automated guide-way transit system are investigated by analytical approach of bridge-vehicle interaction. In this study, the dynamic responses, concerned with a spall on the surface of bridge are emphasized. A simply supported pre-stressed concrete bridge is adopted as a numerical example. Dynamics of three-dimensional dynamic interaction system between bridges and vehicles is considered in this study. The FE method and modal analysis is used for modeling a bridge for dynamic response analysis. An AGT vehicle is idealized as a model with 11DOFs including lateral motion. It was found that the dynamic responses of bridge can be affected by a spall of surface. Especially, the vibrations are increased much more when a spall is exist.

• Explosives and Blasting
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• v.23 no.3
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• pp.19-25
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• 2005

Ground vibration, sound pressure and fragmentation size were measured at the construction site using the spall blasting method(SBM). nev were analyzed and compared to those or suggested method by the minister of construction and transportation(MOST). Vibration and sound pressure by SBM were slightly smaller than MOST method but fragmentation size were larger.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.3
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• pp.89-97
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• 2002

In this study, the effect of $\alpha$-phase morphology on the dynamic deformation behavior at ultra high strain rate was investigated by EBW(Explosive Bridge Wire) test. All of tests and analyses were conducted on three typical microstructures of Ti-6Al-4V alloy, i.e. equiaxed, widmanstatten and bimodal microstructures. The spall strength and HEL(Hugoniot Elastic Limit) of the specimens that have the thickness of 2mm and 4mm were highest with the bimodal microstructure. These results were similar with previous study which was performed by dynamic torsion test(Kolsky torsion test).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.11a
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• pp.38-39
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• 2013

In this study, in regard to fiber reinforced mortar mixing steel fiber and 4types of organic fiber, impact test was carried out. Because to predict fracture reduction performance with flexural, tensile strength when types of fiber were different as impact reduction performance of concrete is closely related with toughness such as flexural strength, tensile strength and fracture energy etc. As a result, enhancement of toughness by fiber reinforcement controls the spall of rear. On the other hand in case of steel fiber relatively turned up high toughness in appropriate load compared with organic fiber but in same mixing rate, impact reduction performance by projectile showed low performance due to few number of an individual of mixing.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.1
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• pp.37-45
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• 2013

In this study, the resistance of various reinforced concrete (RC) slabs subjected to contact detonation was assessed. In order to enhance the blast resistance, fibers and external FRP sheets were reinforced to RC slabs. In the experiment, the $2,000{\times}1,000{\times}100mm$ sized RC slabs were fabricated using normal concrete (NC), steel fiber reinforced concrete (SFRC), polyvinyl alcohol fiber reinforced cementitious composite (PVA FRCC), and ultra-high performance cementitious composites (UHPCC). The damage levels of RC slabs subjected to contact detonation were evaluated by measuring the diameter and depth of crater, spall and breach. The experimental results were compared to the analyzed data using LS-DYNA program and three different prediction equations. The diameter and depth of crater, spall and breach were able to be predicted using LS-DYNA program approximately. The damage process of RC slabs under blast load was also well expressed. Three prediction equations suggested by other researchers had limitations to apply in terms of empirical approaches, therefore it needs further research to set more analytical considerations.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.475-476
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• 2009

This study investigates fundamental and spalling resistance properties of high strength concrete, W/B 28%, designed with the various diameters and contents of PET fiber. The flowability and compressive strength showed similar tendency in the range of below 0.05vol.% of fiber content. For the spalling resistance properties after fire test, the specimens with 40${\mu}m$ diameters of fiber did not spall at 0.05vol.% of fiber content. And the specimens with 20${\mu}m$ diameters of fiber did not spall even at 0.03vol.% of fiber content.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1759-1772
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• 1994

In this study, we re-examined the Tate's modified Bernoulli equation to study penetration phenomena for long rod projectile into single or multi-layered finite thickness plates. We used the force equlibrium equation at mushroomed nose/target interface instead of conventional pressure equation at the stagnation point. In our penetration model, we considered the velocity dependent $R_t$ value for semi-infinite target and considered only the back face effect for finite target. To compensate for $R_t$ value according to target's thickness and back face effect, we used the spherical cavity expansion theory for semi-infinite plate and used the cylindrical cavity expansion theory for finite plate. Also we developed the experimental technique using make screen to measure the penetration duration time at each layered plate. In 3-layered laminated RHA/mild steel/ A1 7039 plate, we observed that spall had occured around the back face of A1 7039 plate by the stress wave interaction. Through the comparison between theoretical and experimental data including Lambert's results, we conform that our study has good confidences.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.169-177
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• 2017

In this paper, classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition(EEMD) for the gear transmission error(TE). Finite element models of the gears with the two faults are built, and TE is obtained by simulation of the gears under loaded contact. EEMD is applied to the residuals of the TE which are the difference between the normal and faulty signal. From the result, the difference of spall and crack faults are clearly identified by the intrinsic mode functions(IMF). A simple test bed is installed to illustrate the approach, which consists of motor, brake and a pair of spur gears. Two gears are employed to obtain the TE for the normal, spalled, and cracked gears, and the type of the faults are separated by the same EEMD application process. In order to quantify the results, crest factors are applied to each IMF. Characteristics of spall and crack are well represented by the crest factors of the first and the third IMF, which are used as the feature signals. The classification is carried out using the Bayes decision theory using the feature signals acquired through the experiments.