INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2024, VOL. 70, NO. 1, PP. 45–50
Manuscript received November 14, 2023; revised March, 2024.
doi: 10.24425/ijet.2024.149513

Smart non-destractive test of a concrete
wall using a hammer
Atsushi Ito, Masafumi Koike, and Katsuhiko Hibino

Abstract—Large concrete structures such as buildings, bridges,
and tunnels are aging. In Japan and many other countries,
those built during economic reconstruction after World War II
are about 60 to 70 years old, and flacking and other problems
are becoming more noticeable. Periodic inspections were mademandatory by government and ministerial ordinance during
the 2013-2014 fiscal year, and inspections based on the newstandards have just begun. There are various methods to checkthe soundness of concrete, but the hammering test is widely usedbecause it does not require special equipment. However, longexperience is required to master the hammering test. Therefore,mechanization is desired. Although the difference between thesound of a defective part and a normal part is very small, we
have shown that neural network is useful in our research. To
use this technology in the actual field, it is necessary to meet theforms of concrete structures in various conditions. For example,
flacking in concrete exists at various depths, and it is impossible
to learn about flacking in all cases. This paper presents the resultsof a study of the possibility of finding flacking at different depths
with a single inspection learning model and an idea to increase
the accuracy of a learning model when we use a rolling hammer.
Keywords—Hammering test; Non-destructive test; Neural network; Transfer learning

I. I NTRODUCTIONI

N recent years, social infrastructures such as buildings,
bridges, and roads have been aging, and the demand forinspection is expected to increase [1]. According to theJapanese government report, at the end of 2022, approximately
1,257,000 condominium units were 40 years old or older. This
number is expected to increase by 2.1 times in 10 years and3.5 times in 20 years [2].
Infrastructure inspection encompasses a range of maintenance tasks, necessitating meticulous diagnosis and assessment of structural integrity and seismic resilience. Modern
techniques involve using robots and drones to inspect areas
inaccessible to humans, such as expansive bridges and highrise walls, often necessitating scaffolding [4]. In cases wherevisual inspection is impossible, like within building walls, acommon method involves detecting changes in sound throughThis study is funded by JSPS Kakenhi grants (17H02249, 18K111849,
20H01278, 20H05702, 22K12598, 23H03649).
A. Ito is with Faculty of Economics, Chuo University, Tokyo, Japan(ORCID: 0000-0002-9686-4019, e-mail: atc.00s@g.chuo-u.ac.jp).
M. Koike is with Department of Engineering, Utsunomiya University,
Tochigi, Japan (e-mail: koike@is.utsunomiya-u.ac.jp).
K. Hibino is with Port Denshi Corporation, Tokyo, Japan (e-mail:hibino@port-d.co.jp).

Fig. 1. Types of structure and surface

Fig. 2. Flacking caused by salt

hammering. Despite being a cost-effective technique, it demands expertise to discern sounds indicating structural issues,
making judgments subjective and potentially inconsistent.
Despite its affordability, this testing method demands expertise in differentiating sounds originating from defective
areas. However, judgments are subjective and can vary due toindividual perceptions. Sound testing is not limited to concrete
walls but extends to tile walls commonly found in apartments,
numbering approximately 6.65 million in Japan [3]. Streamlining and simplifying hammering tests are vital to inspect thissubstantial number of buildings efficiently.
Figure 1 shows several types of buildings and structures.

Fig. 3. Flacking caused by alkaline aggregate reaction© The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0,
https://creativecommons.org/licenses/by/4.0/), which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited.

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The structure’s body is often made of concrete, although
in some cases, it may consist of stone or brick, which is the
subject of this paper. Two surface patterns exist. Large structures are bare concrete, but condominiums and commercial
buildings are often covered with tiles to decorate and protect
the concrete. In the past, tiles were often applied with mortar,but in recent years, tiles are increasingly being applied withadhesives. In this paper, our focus is on a structures with
exposed concrete surfaces.
Figure 2 (caused by salt) and Figure 3 (caused by alkaline
aggregate reaction) show two major reasons that cause flacking
in the concrete constructions. In both cases, small clacks
caused the inside of a concrete wall to grow and increase
according to the year. Finally, small clacks cause flacking,and in the worst case, some portion of concrete falls.
In this paper, we discuss the outline of the research on
the hammering test of concrete walls. There are differences
between tile and concrete walls, as shown in Table I. A
significant difference is that the tiles’ flaking (cavity) locationis almost the same depth, a few millimeters down. However,concrete walls vary from 20 mm to 100 mm or even morein some cases. For this reason, we made test blocks withflacking with different depths of flaking (25mm and 55mm).
In contrast, concrete walls do not vary in material and texture
from place to place, but the depth at which flacking varies.
However, developing a learning model using training dataof multiple depths of flacking takes much work. Notably,
differences between tile and concrete walls, detailed in Table I,
are significant. Tiles typically exhibit consistent flaking depths,
a few millimeters down, whereas concrete walls vary widely,
ranging from 20 mm to 100 mm or even more in certain cases.
We created test blocks with different flaking depths (25mm
and 55mm) to address this variation.
In the rest of this paper, we explain the related works in
section 3 and the research questions in section 4. In section
5, the result of the research of the possibility of realizing the
one learning model can be used for the flacking of different
depths. Section 6 explains the trial result to solve the problem
of using a rolling hammer to investigate concrete walls. We
conclude this paper in section 7.
II. H AMMERING TEST AND AI
There are two devices and an inspection method to find theflacking of concrete walls. As shown in Figure 4, two devices
are used for hammering tests: one is a small hammer (left),and another is a rolling hammer (right).
The rolling hammer has a metal hexagonal ball. The gap
between the two edges measures 1.6 cm. A hammering soundis generated by the rolling action of this device along the wall.
The sounds are captured using a microphone positioned near
the rolling ball.
In Figure 5, an instance of a hammering sound obtained witha KoroKoro is illustrated. The advantage of using KoroKoro
lies in its ability to strike a wall approximately 15 times per
second (roughly every 0.07 seconds). In contrast, a standard
hammer yields a hammering sound about once per second.
So, we have used neural networks (NN) and Transfer
Learning (TL) [9] for the hammering test to check the

Fig. 4. Two devices for hammering test

Fig. 5. Recorded sound by using a rolling hammer

injured part by using the hitting sound. We targeted a tile walland demonstrated our approach’s effectiveness for tile walls
commonly used in apartments [10].
The TL is a way to develop a learning model quickly bymodifying the last layer of the existing learning model andis used frequently in many applications. One of the famousenvironments to use TL is a Teachable Machine [7] [8]. The
Teachable Machine uses MobileNet [19] as the basis of thelearning.
The TL offers a rapid approach to developing learningmodels by adjusting the final layer of an existing model. It
finds widespread application in various fields. A well-known
example of TL implementation is the Teachable Machine [7][8], which utilizes MobileNet [19] as its foundational learning
framework.
Concrete walls, while consistent in material and texture
across different locations, vary significantly in flaking depth.
Designing a learning model utilizing training data from various
flaking depths demands substantial effort.
Therefore, this paper presents the findings of a study aimed
at constructing a learning model unaffected by the depth offlaking. And devices for the hammering test are different. We
can use both a small and rolling hammer for the concrete since
they can cause a strong impact. On the other hand, we can use
a small hammer for a tile wall but not to make an impact. We
slide a small ball on the top of a small hammer. We cannot
use a rolling hammer since a strong impact sometimes breaks
a tile.
III. R ELATED W ORKS
Significant research has been conducted on AI-applied hammering testing in the mentioned scenario. In their study [11],various algorithms, including SVM and DT, were compared.
The SVM method exhibited a worst-case predictive value of
72% and a best-predicted value of 99%. Another study [12]

SMART NON-DISTRACTIVE TEST OF A CONCRETE WALL USING A HAMMER

47

TABLE ID IFFERENCE OF CHARACTERISTICS BETWEEN WALLS MADE BY TILE AND CONCRETE
Features
Finish of the surface
Location of flacking
Depth of flacking
Flacking size
Effect of Rolling Hammer

Tile

Concrete

Surface textures are different
Between tiles, there is mortal partThe size of each tile is almost the same
Between a tile and wall
Tile thickness is about the same
Smaller than the tile size
Sometimes Rolling Hammer is trapped between tiles
and slips on the surface

utilized a camera and SVM for hammering tests, achieving an
F-measure of approximately 0.73.
At the product level, tools like T.T.Car [5] and AI Hammering Test Checker [6] employ hammering tests. T.T.Car
creates a problem area map by moving along measurement
lines drawn on the road but cannot inspect wall surfaces.
AI Hammering Test Checker records hammer sounds froma microphone and employs a machine learning function to
detect flaking or peeling inside concrete walls using the kmean method as an ML algorithm. However, its accuracy does
not exceed 80%, whereas the accuracy reaches 90% whenusing NN and Transfer Learning [9].
There are various methods for the inspection of structures,
including those using microwave ( [13]), robots ( [14]), andultrasonic waves ( [17]). In reference [15], various techniques
related to nondestructive inspection of tunnel linings arepresented.
The application of machine learning to structural inspection has gained momentum in recent years. Reference [11]compares the effectiveness of applying machine learning algorithms such as Support Vector Machine (SVM) and Decision
Tree (DT). In the case of SVM, the performance is 99% when
it is good, but when it is bad, it is about 72%. According to
the [12], in the case of multimodal, images and percussion
are combined, and a decision using SVM is put into it. The F
value, in this case, is shown to be about 0.73. In addition to
our work ( [9]), other deep learning applications include thedevelopment of a technique for detecting cracks in concrete[16] and the use of transition learning to detect cracks inmasonry walls [18].
T.T.Car [5] and AI hammering test checker [6] are among
those that have been commercialized. T.T.Car moves along aline drawn on the road and detects normal areas and areas
with floaters or cracks while performing a hammering test. AI
hammering test checker collects hammering sounds of normal
locations collected by a microphone and uses the k-mean
method to construct a decision logic for each use. However,its accuracy could be higher, around 80%. On the other hand,by using neural networks and transition learning, which wehave been doing, it has been shown that an accuracy of about
90% can be achieved [9].
IV. R ESEARCH Q UESTIONSThere are several difficulties in performing hammering tests.
1) The sound caused by the flacking part is small, andusually, the sound of the healthy and flacking parts is
not large. Many experiences and training are required.

SmoothFlat
Inside concrete
The depth of flaking varies a lot
The size of flaking varies a lot
Rolling Hammer roles smooth and uniformly

2) If a hammer hits a wall hard, the returned sound is large,
but sometimes the hammer destroys or creates anotherproblem in a wall. So, it is required to make an impact
not so hard.
3) Many large buildings and constructions face large streets.
The road noise is large, and it is difficult to listen to small
sounds caused by the flacking part. The night operation
at the high wall is dangerous, so we must inspect the wallin the daytime.
So, we need to develop a technique to inspect a wall using
AI.
This paper examines the following issues in the acoustic
inspection of concrete structures.
1) Can a learning model work for the flacking at different
depths?
2) Can we prevent the loss of accuracy in the case ofcontinuous data acquisition, as in the case of a rolling
hammer, instead of using a small hammer?
For this purpose, the following experiments were conducted.
1) With a specimen with delamination at 55 cm and 25 cm
can the model learned with one recognize the other?
2) Analysis of the data acquired when using the rolling
hammer and how to respond to the data.
In this experiment, data is obtained using a small hammer
and a rolling hammer, as shown in Figure 6, then manually
separated into normal (healthy) and flacking parts. The specimen is structured as shown in Figure 8 and Figure 10 witha circular flacking area and a normal area around it. Whenacquiring data from these parts, if a small hammer is used,
the normal and abnormal parts are disjointed, and the datafrom the normal and flacking parts are independent, as shownin Case 1 of Figure 7. It means that the obtained learningmodel is stable.
In contrast, when using a rolling hammer, the hammer
passed the normal and flacking parts through the boundaryof the normal and flacking portions, as shown in Figure 8.
Therefore, as shown in the Figure 8 and Figure 9, some places
show normal and flacking responses. As shown in Case 2of Figure 7, parts of the response are difficult to distinguishbetween the two, even when creating data for learning. It
means that the learning data set is not disjoint, and suchdata may cause the learning model to contain noise, lower thepercentage of correct answers, and not be stable. This kind ofdata is thought to cause the learning model to contain noiseand lower the rate of correct answers.
Chapter 4 below describes our experiments’ results on
responses to different depths’ flackings. In Chapter 5, we48

A. ITO, M. KOIKE, K. HIBINO

describe the results of our investigation into data handling at
the boundary between normal and flacking areas.

Fig. 9. The wave caused by rolling hammer

Fig. 6. A flow for experimentFig. 10. The structure of specimen– Extract data randomly from the hammering result. For
developing the learning model, we used 56 training and
8 test data.
– Perform training with 150 epochs using Transfer Learning(Teachable Machine [7] [8]).
– Perform the test using four normal and four flacking.

Fig. 7. The effect of the boundary to create a learning model

We achieved an accuracy of 91.2% for the 55mm flaking
depth, as illustrated in Figure 12. Subsequently, we assessedthe versatility of the learning model for specimens with 25mm
flaking depth, employing 10 test samples from a specimen withflaking at 25mm. The accuracy, depicted in Figure 12, stoodat 97.3%.
These results indicate that the learning model designed fora 55mm flaking depth specimen has the capability to test
data from various flaking depths. The learning model wasdeveloped using Teachable Machine.
VI. A DAPTING ROLLING H AMMER
Then, we evaluated the model as explained in the previous
section using the test specimens in Figure 13 and used a rolling
hammer to detect the flacking. This specimen has flacking at
40mm, 60mm, 80mm, and 100mm deep.

Fig. 8. The border data between the normal part and flacking part

V. A DAPTING D IFFERENT D EPTH OF F LACKING
This section presents the outcomes of validating the accuracy of the hammering test conducted on the sample illustrated
in Figure 11.
The test performed on the specimen with flaking 55 mm
below the surface is outlined as follows.

Fig. 11. The specimen for the test of depth

SMART NON-DISTRACTIVE TEST OF A CONCRETE WALL USING A HAMMER

Fig. 12. The result of use one learning model to different specimen

We conducted tests and gathered data from the concrete block. Regrettably, the outcome fell below expectations(around 50%). Consequently, we meticulously analyzed thehammering sound data to discern the cause behind this unexpected result.

49

Fig. 15. Comparison of FFT of normal and flacking partWe labeled each sound caused by hitting as normal or
flacked. However, as shown in Figure 9, between the normalpart and flacking part, there is a sound caused by the border
between the normal part and the flacking part. As shown in
Figure 9, if a hammer hits the border between the normal partand the flacking part, it is not easy to distinguish the soundthat comes from the normal part or the flacking part. In thefollowing part of this paper, we call the sound of the normalpart type a, the flacking part type b, and the border part typec.
A. Analyse the sound data

Fig. 13. Different sample

Figure 14 displays typical patterns of hammering sound data
in WAV format, showcasing four distinct types.
Data from the normal section (type a), the flaking area
(type b), and the boundary between the normal and flaking
sections (type c) exhibit varying characteristics. The soundfrom the flaking section (type b) is louder, with a peak at 1.0,
in contrast to the normal section (type a), where the soundregisters at 0.4 to 0.5. This discrepancy arises because theconcrete in normal areas is solid and does not vibrate, while
the flaking section, having a cavity, produces a louder, moremusical sound. Additionally, the flaking part’s waveform isthicker due to the presence of vibration sounds within it.
We also examined the sound from the border region (type c).
The sound’s peak resembles that of type a and type c. However,type c exhibits some vibrations caused by the flaking part. The
waveform of type c includes some noise in comparison to typeb but remains indistinct.

Fig. 14. Comparison of sound (wav) of normal and flacking partB. Analyse the waveforms of FFT
Figure 9 displays the output from a rolling hammer. The
sound produced by a normal section (shown in the figure’s
center) was smaller than that of the flaking part. Although
the power of the peak remained consistent, the flaking section
exhibited a larger and non-uniform peak. In this scenario, theactual flaking area is at the center (marked by the loud sound),while the surrounding regions (flatter portions) are normal.
Consequently, the sound’s intensity plays a crucial role indistinguishing flaking.

The waveforms obtained after the Fast Fourier Transform(FFT) were compared, as depicted in Figure 15. Typically,
the flaking section (type a) exhibits two peaks, attributed
to the cavity reflecting vibration sounds within the wall. In
contrast, the normal section (type b) lacks a second peak.
However, identifying the border section poses a challenge.
This area displays distinct characteristics, including additional
vibrations, although they are smaller than those in type a.
Distinguishing these nuances proves to be difficult.

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A. ITO, M. KOIKE, K. HIBINO

C. Removing data of border for learning
Distinguishing type c data as flaking proved challenging.
Therefore, we manually removed such data to enhance thequality of our learning model. Utilizing the Teachable Machine, we developed the model, the results of which arepresented in Table II. The right column indicates the outcomeof the learning model created without type c data.
Upon eliminating type c data, the learning model exhibited
improved accuracy.
TABLE IIT RIAL WITHOUT TYPE 3 C DATAThe number of learning data
(Normal/Flacking)
The number of test data
(Normal/Flacking)Accuracy

type c included

type c removed
from learning data

40 (20/20)

40 (20/20)

20 (10/10)

20 (10/10)

90%

95%

VII. C ONCLUSIONSIn Japan and worldwide, social infrastructure such as roads,
bridges, dams, and other buildings constructed immediately
after World War II are aging, and accidents involving concrete
spalling are becoming more common. Also, the number ofpeeling tiles attached to residential buildings for decoration
is increasing. Therefore, we have been studying techniques toinspect them.
This paper shows that the model learned on a 25 mm deep
crack can also be used on a 55 mm deep crack. This result
demonstrated the versatility of the learning model and thepracticality of using neural networks for hammering tests. The
accuracy obtained from our experiment was over 90%. This
result was better than the accuracy of AI Hammering Test
Checker [6] that used the k-mean method. However, this result
was obtained in the limited number of test data. Improving
the training data’s accuracy is necessary to construct a moreversatile learning model. We showed that the accuracy canbe increased by eliminating data at the boundary between
abnormal and normal areas.
For future work, we intend to verify the model’s usefulness
by testing it on various specimens and concrete structures.
Another issue to be addressed for practical application is thedevelopment of technology to locate flacking areas accurately.
In addition, while we used recorded data for the research, realtime processing is required to use this system in the real field.
The construction of software to handle this is also an issue.
ACKNOWLEDGMENT
The authors express their gratitude to Mr. Utagawa of
Sato Kogyo Corporation for assisting in collecting soundsamples from the concrete testing structure. The authors also

thank Mr.Fukumura, who joined the research to use TL for
the hammering test when he was a student at Utsunomiya
University.
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