applied sciences Article
Using Ultrasonic Pulse and Artificial Intelligence to Investigate the Thermal-Induced Damage Characteristics of Concrete Li-Hsien Chen 1 , Wei-Chih Chen 2, *, Yao-Chung Chen 2 , Hsin-Jung Lin 2 , Chio-Fang Cai 3 , Ming-Yuan Lei 3 , Tien-Chih Wang 3 and Kuo-Wei Hsu 4 1 2 3 4
*
Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;
[email protected] Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan;
[email protected] (Y.-C.C.);
[email protected] (H.-J.L.) Architecture and Building Research Institute, Ministry of the Interior, Taipei 23143, Taiwan;
[email protected] (C.-F.C.);
[email protected] (M.-Y.L.);
[email protected] (T.-C.W.) Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan;
[email protected] Correspondence:
[email protected].; Tel.: +886-273-331-41 (ext. 7516)
Received: 30 May 2018; Accepted: 6 July 2018; Published: 9 July 2018
Featured Application: The developed wave velocity ratio measured using the ultrasonic pulse technique can be used to identify the degree of thermal-induced damage in concrete. It has a potential applying to investigate the degree of damage to buildings and the maximum fire temperature after a fire disaster. Abstract: Using the traditional assessment method considering single-input and single-output variables, the correlation between ignition loss and maximum temperature is usually used to evaluate the fire-damage degree of concrete. To improve this method, multi-input and multi-output variables are examined in this study using a newly-developed experiment consisting of a thermo-induced damage test, ultrasonic pulse (UP) measurement technique, and uniaxial compressive test. The input variables include the designed strength, rate of heating, maximum temperature, and exposure time. The output variables include the stiffness, strength, toughness, and ratio of shear wave velocity to pressure wave velocity (Vs /Vp ). Artificial intelligence (AI) is used to assess these variables. The test results show that the stiffness, strength, and toughness decreased with an increase in maximum temperature. The measured Vs /Vp has a high positive correlation with maximum temperature and the reduced ratio of stiffness, strength, and toughness. This correlation was also identified using AI analysis. The findings in this study suggest that the wave velocity ratio obtained using the UP technique can be applied to quantitatively evaluate thermal-induced damage in concrete. Keywords: thermal-induced damage; fire disaster; ultrasonic pulse; wave-velocity ratio; artificial intelligence; WEKA
1. Introduction A reinforced concrete (RC) structure is the main system for buildings and construction in Taiwan. The quality/quantity evaluation of fire-induced damage in RC structures is important for living safety. Tovey [1] proposed several methods such as the color change identification method, core experiment, ultrasonic pulse (UP) measurement, and the correlation of concrete strength reduction with temperature to evaluate the degree of fire-damage. However, the color change identification Appl. Sci. 2018, 8, 1107; doi:10.3390/app8071107
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method, core experiment, and UP measurement are not widely used due to their low accuracy [2]. The correlation between the ignition loss (weight loss) and the maximum fire temperature was usually investigated and used to evaluate the degree of fire-damage in past decades [3,4]. With the development of science and technology, the UP technique with a high accuracy has been developed and used as a non-destructive detection technique. However, the low correlation between pressure wave velocity (Vp ) measured using UP measurement and uniaxial compressive strength was found by some researchers [5–8]. Artificial Intelligence was then used to construct a model of Vp and strength relation [9,10]. In this study, the correlation between UP wave velocity ratio Vs /Vp (ratio of shear wave velocity to pressure wave velocity) and the maximum temperature, reduced ratio of stiffness, strength, and toughness of concrete subjected to thermal damage was investigated. A new thermo-solid damage experiment that consisted of the thermo-induced damage test, UP measurement technique, and uniaxial compressive test was established. Furthermore, to identify these experimental results, the data mining/machine learning method was used to evaluate the correlation between thermal conditions (the rate of heating, maximum temperature, exposure time, as well as cooling condition), mechanical parameters (stiffness, strength, and toughness), and the UP wave velocity ratio. 2. Mechanical Behavior of Thermal-Induced Damage in Concrete The thermal-induced damage process in concrete could be defined as the rate of heating, maximum temperature, exposure time, and cooling method. The effect of these four conditions on the mechanical behavior of concrete is defined as follows. 2.1. Heating Rate (Rheat ) The temperature difference between the surface and the center of the specimen generates thermal stress, causing crack formation. With a high heating rate, more cracks are formed, thus reducing the concrete strength. This mechanical behavior was observed by Anderberg and Thelandersson [11] who conducted compressive testing with different heating rates for concrete. 2.2. Maximum Temperature (Tmax ) The maximum temperature is the main factor that reduces concrete strength. High temperature makes the bonded water between the cement and particle material disappear and results in particle phase change. A crack is formed at the interface of these materials within the concrete. In general, with higher maximum temperature treatment, a lower stiffness and strength are produced [3,12]. 2.3. Exposure Time (Etime ) Mohamedbhai [13], who conducted the heat-driven-damage test with different maximum temperatures (200, 400, 600, and 800 ◦ C) and exposure times (1, 2, 3, and 4 h), indicated that concrete strength was reduced at the maximum temperature range from 300 ◦ C to 600 ◦ C with a 2 h exposure time. 2.4. Cooling Method (Mcool ) After the heat treatment, different cool down rates will also have different temperature effects on the specimen surface and center, inducing secondary damage in the concrete. The three cooling methods include cooling in a furnace, cooling in air, and cooling in water. 3. Thermal-Solid Damage Experiment Thermal-solid damage experiments were conducted to investigate the thermal-induced characteristics of concrete. The uniaxial compressive test (stress-induced damage) was conducted after specimens were subjected to heating (thermal-induced damage) under different thermal-conditions (Table 1). Tests were designed based on the mechanical behavior of thermal-induced damage described
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conditions (Table 1). Tests were designed based on the mechanical behavior of thermal‐induced damage Section 2. the During the testing, the circumferential extensometer was used to in Sectiondescribed 2. Duringin the testing, circumferential extensometer was used to stabilize crack growth. stabilize crack growth. Furthermore, after heating treatment, ultrasonic pulse measurement was used Furthermore, after heating treatment, ultrasonic pulse measurement was used to find the correlation to find the correlation between UP waves and mechanical properties. between UP waves and mechanical properties. Table 1. Experimental conditions and measurement. Table 1. Experimental conditions and measurement.
Material Material
Thermal-Solid Damage Method Thermal‐Solid Damage Method
Measurement Measurement
Concrete qu = 70 MPa (designed strength) Concrete qu = 70 MPa (designed strength) heating rate Rheat = 5 °C/min ◦ heating rate Rheat = 5 C/min maximum Tmax = 25, 200, 300, 400, 500, 600, 800, Tmax = 25, 200, 300, 400, 500, 600, thermal-induced thermal‐ maximum temperature temperature 1000, 1200 °C 800, 1000, 1200 ◦ C induced exposure time E time = 300 min exposure time Etime = 300 min cooling method M cool :cool down in furnace cooling method Mcool :cool down in furnace obtaining the stiffness, strength, and obtaining the stiffness, strength, stress‐ uniaxial stress-induced uniaxial compressive test and toughness after toughness after thermal‐induced induced compressive test damage thermal-induced damage ultrasonic pulse (UP) measure: VS , VP , VS /VP S, VP, VS/VP ultrasonic pulse (UP) measure: V
3.1. Thermo-Induced-Damage Concrete Specimen 3.1. Thermo‐Induced‐Damage Concrete Specimen The concrete produced by Ya ReadyReady MixedMixed Concrete Corporation in Taiwan The 70 70 MPa MPa strength strength concrete produced by Tung Ya Tung Concrete Corporation in was used as the test material. Considering the size effect and end effect, the specimens were fabricated Taiwan was used as the test material. Considering the size effect and end effect, the specimens were into 50 mm × 45 mm × 120 mm cubes, as shown in Figure 1a. The cube specimens were then placed fabricated into 50 mm × 45 mm × 120 mm cubes, as shown in Figure 1a. The cube specimens were in an oven at 105 ± 5 ◦ C and remained there for 24 h to produce void water dissipation. The prepared then placed in an oven at 105 ± 5 °C and remained there for 24 h to produce void water dissipation. specimens were used to simulate thermal-induced damage with different temperature conditions The prepared specimens were used to simulate thermal‐induced damage with different temperature in a high temperature oven (Figure 1b). When the thermal-induced damaged specimen cooled, conditions in a high temperature oven (Figure 1b). When the thermal‐induced damaged specimen ◦ C/min, exposure time the specimen was stored in vacuum packaging. The heating rate Rheat = 5heat cooled, the specimen was stored in vacuum packaging. The heating rate R = 5 °C/min, exposure E = 300 min, and maximum temperature T = 25, 200, 300, 400, 500, 600, 800, and 1000 ◦ C. max max = 25, 200, 300, 400, 500, 600, 800, and 1000 °C. time time = 300 min, and maximum temperature T time E
(a)
(b)
Figure 1. 1. Thermo-induced-damage Thermo‐induced‐damage concrete high Figure concrete specimen specimen preparation: preparation: (a) concrete specimen; (a) concrete specimen; (b) (b) high temperature oven. temperature oven.
3.2. Stress‐Induced Damage Test (Uniaxial Compressive Test) 3.2. Stress-Induced Damage Test (Uniaxial Compressive Test) Uniaxial compressive tests were conducted after thermal‐induced damage treatment. The Uniaxial compressive tests were conducted after thermal-induced damage treatment. multifunction, precision, high‐stiffness servo‐controlled hydraulic test system MTS 810 with a 1MN The multifunction, precision, high-stiffness servo-controlled hydraulic test system MTS 810 with output force capacity was adopted as the load system. A circumferential extensometer (model a 1MN output force capacity was adopted as the load system. A circumferential extensometer 632.92F‐05C, MTS Corp., Eden Prairie, MN, USA) was installed through the roller chain (Figure 2) (model 632.92F-05C, MTS Corp., Eden Prairie, MN, USA) was installed through the roller chain and used to obtain the entire load curve. Data recorded by the extensometer indicates changes in the (Figure 2) and used to obtain the entire load curve. Data recorded by the extensometer indicates circumference of the specimen during the test. The length increment was used as a feedback signal changes in the circumference of the specimen during the test. The length increment was used as to automatically control the load output. a feedback signal to automatically control the load output.
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Figure 2. Uniaxial Compressive test with a circumferential extensometer. Figure 2. Uniaxial Compressive test with a circumferential extensometer. Figure 2. Uniaxial Compressive test with a circumferential extensometer. 3.3. Ultrasonic Pulse Measurement 3.3. Ultrasonic Pulse Measurement
3.3. Ultrasonic Pulse Measurement The ultrasonic pulse pulse measurement principle to generate a vibration source with a fixed The ultrasonic measurement principle is to is generate a vibration source with a fixed frequency frequency by driving the piezoelectric material within the ultrasonic transducer (Figure 3b,c). When by driving the piezoelectric material within the ultrasonic transducer (Figure The ultrasonic pulse measurement principle is to generate a vibration source 3b,c). with a When fixed the the transmitting transducer makes contact with one side of the test specimen, the vibration source is frequency by driving the piezoelectric material within the ultrasonic transducer (Figure 3b,c). When transmitting transducer makes contact with one side of the test specimen, the vibration source is the transmitting transducer makes contact with one side of the test specimen, the vibration source is transmitted within the specimen material as a stress wave and received by the receiving transducer transmitted within the specimen material as a stress wave and received by the receiving transducer on transmitted within the specimen material as a stress wave and received by the receiving transducer on the other the specimen. If the detected is the known, wave can velocity can be the other side side of theof specimen. If the detected distancedistance is known, wavethe velocity be calculated. on the other side of the specimen. If the detected distance is known, the wave velocity can be calculated. The speed of wave propagation will be affected by the type of medium. It is prevalent that the calculated. fewerThe speed of wave propagation will be affected by the type of medium. It is prevalent that the the voids and defects within the material, the higher the wave transmission speed [14–16]. The speed of wave propagation will be affected by the type of medium. It is prevalent that the fewer the voids and defects within the material, the higher the wave transmission speed [14–16]. It It should be noted that the generated stress wave type can be a pressure wave (P wave) defined as the fewer the voids and defects within the material, the higher the wave transmission speed [14–16]. It should be noted that the generated stress wave type can be a pressure wave (P wave) defined as the particles in the material vibrating along the wave propagation axis, or a shear wave (S wave) defined should be noted that the generated stress wave type can be a pressure wave (P wave) defined as the particles in the material vibrating along the wave propagation axis, or a shear wave (S wave) defined as wave vibration direction perpendicular to the wave propagation direction. particles in the material vibrating along the wave propagation axis, or a shear wave (S wave) defined as wave vibration direction perpendicular to the wave propagation direction. Ultrasonic pulse measurement was first used to detect defects in metal material in 1929. At present, as wave vibration direction perpendicular to the wave propagation direction. measurement was used to detect detect defects metal material in 1929. Ultrasonic pulse was first first used to defects in in metal material in 1929. At At it hasUltrasonic been used pulse widely inmeasurement civil engineering to evaluate rock quality, species, strength, and properties present, it has been used widely in civil engineering to evaluate rock quality, species, strength, and present, it has been used widely in civil engineering to evaluate rock quality, species, strength, and including density, porosity, and elastic modulus [17–19]. This study is the first attempt at using properties including density, porosity, and elastic modulus [17–19]. This study is the first attempt at properties including density, porosity, and elastic modulus [17–19]. This study is the first attempt at ultrasonic pulse measurement to examine the thermal-induced damage level of concrete. Ultrasonic using ultrasonic pulse measurement to to examine the damage level of concrete. using ultrasonic pulse the thermal‐induced thermal‐induced damage level of concrete. pulse measurement wasmeasurement practiced in this examine study using the UK-1401 dry point ultrasonic tester with Ultrasonic pulse measurement was practiced in this study using the UK‐1401 dry point ultrasonic Ultrasonic pulse measurement was practiced in this study using the UK‐1401 dry point ultrasonic P/S wave transmitted and received transducers from Acoustic Control Systems (ACS), as shown in tester with P/S wave transmitted and received transducers from Acoustic Control Systems (ACS), as tester with P/S wave transmitted and received transducers from Acoustic Control Systems (ACS), as Figure 3. The measured wave velocity and distance of UK-1401 ranged from 1000 to 9990 m/s and shown in Figure 3. The measured wave velocity and distance of UK‐1401 ranged from 1000 to 9990 shown in Figure 3. The measured wave velocity and distance of UK‐1401 ranged from 1000 to 9990 from 50 to 250mm, respectively. m/s and from 50 to 250mm, respectively. m/s and from 50 to 250mm, respectively.
(b)
(b)
(a)
(c)
Figure 3. Dry point ultrasonic tester:(a) (a) device; (b) pressure wave transducer Figure 3. Dry point ultrasonic tester: (a) device; (b) pressure wave transducer of 100 kHz; (c) shear (c) of 100 kHz; (c) shear wave wave transducer of 50 kHz. transducer of 50 kHz.
Figure 3. Dry point ultrasonic tester: (a) device; (b) pressure wave transducer of 100 kHz; (c) shear wave transducer of 50 kHz.
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4. Machine Learning
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To find the correlation between the input and output variables in the thermal-induced damage 4. Machine Learning experiment (Table 1), in this study, WEKA (Waikato Environment for Knowledge Analysis) developed To find the correlation between the input and output variables in the thermal‐induced damage at the University of Waikato, New Zealand, was used as the machine learning/data mining tool [20,21]. experiment in this machine study, WEKA (Waikato Environment Knowledge Analysis) WEKA performs (Table several1), standard learning tasks including datafor preprocessing, classification, developed at the University of Waikato, New Zealand, was used as the machine learning/data mining clustering, regression, feature selection, and visualization. The correlation of variables in WEKA was chosen tool [20,21]. WEKA performs several standard machine learning tasks including data preprocessing, by finding a variable set that gives the maximum value in Equation (1). In Equation (1), correl is the classification, clustering, regression, feature selection, and visualization. The correlation of variables function of correlation, Fi is the i-th input variable, T is the output variable, and Fj is the j-th input variable, in WEKA was chosen by finding a variable set that gives the maximum value in Equation (1). In respectively. In this study, the designed strength, ratei is the i‐th input variable, T is the output variable, of heating, maximum temperature, and exposure Equation (1), is the function of correlation, F time were variables; whereas the wave-velocity ratio, stiffness, strength, and toughness were output and Finput j is the j‐th input variable, respectively. In this study, the designed strength, rate of heating, variables. Correlation is a measurement of the linear relationship between two variables. The target of maximum temperature, and exposure time were input variables; whereas the wave‐velocity ratio, Equation (1) is to find a set of variables that are correlated to the output as much as possible, but correlated stiffness, strength, and toughness were output variables. Correlation is a measurement of the linear relationship between two variables. The target of Equation (1) is to find a set of variables that are to each other as less as possible. Such a set of variables is supposed to be more discriminative. correlated to the output as much as possible, but correlated to each other as less as possible. Such a set of variables is supposed to be more discriminative. ∑i correl ( Fi , T ) , i , Fj ∑i ∑ j,j6=∑i correl F ∑ ∑ ,
,
,
,
(1)
(1)
5. Results and Discussions 5. Results and Discussions
5.1. Experimental Results
5.1. Experimental Results
The uniaxial compressive test was conducted on concrete after heat treatment. During the The uniaxial compressive test was conducted on concrete after heat treatment. During the test, test, the circumference extensometer was used to stabilize the fracture propagation. The complete the circumference extensometer was used to stabilize the fracture propagation. The complete stress‐ stress-strain curve can therefore be obtained. Some basic mechanical parameters such as stiffness, strain curve can therefore be obtained. Some basic mechanical parameters such as stiffness, strength, strength, and toughness can be defined, as shown in Figure 4. In Figure 4a, the peak point (P) and toughness can be defined, as shown in Figure 4. In Figure 4a, the peak point (P) value qu and the value qu and the gradient at the half of qu (point E) were defined as the strength and the stiffness gradient at the half of qu (point E) were defined as the strength and the stiffness (elastic modulus), (elastic modulus), respectively. In addition, the area under the OP curve in Figure 4a can be calculated respectively. In addition, the area under the OP curve in Figure 4a can be calculated as the toughness as theat the pre‐peak stage (T toughness at the pre-peak stage (Tpre ), whereas the area under the OQ curve (Q is the point at pre), whereas the area under the OQ curve (Q is the point at half of q u after half ofpeak) in Figure 4b is the post‐peak toughness (T qu after peak) in Figure 4b is the post-peak toughness (Tpost ). The influence of heat temperature post). The influence of heat temperature on stiffness, on stiffness, strength, and toughness and its relation with the ultrasonic wave is discussed strength, and toughness and its relation with the ultrasonic wave is discussed as follows. as follows.
E(0.5 qu ) 1
P (qu) Stress (MPa)
Stress (MPa)
P (qu)
m
Q (0.5qu)
Tpre
Tpost O
O
Strain (%)
(a)
Strain (%)
(b)
Figure 4. Complete stress‐strain curve: (a) definition of stiffness (E), strength (qu), and pre‐peak
Figure 4. Complete stress-strain curve: (a) definition of stiffness (E), strength (qu ), and pre-peak toughness (Tpre); (b) definition of post‐peak toughness (Tpost). toughness (Tpre ); (b) definition of post-peak toughness (Tpost ).
5.1.1. Relation of Loading Behavior and Heat Temperature
5.1.1. Relation of Loading Behavior and Heat Temperature
Figure 5 shows the complete loading curve with different maximum temperatures obtained in
this study, including the pre‐ and post‐peak loading processes. As expected, the stiffness, strength, Figure 5 shows the complete loading curve with different maximum temperatures obtained in this study, including the pre- and post-peak loading processes. As expected, the stiffness, strength,
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and toughness decreased with an increase in maximum temperature. The reduced stiffness, 6 of 10 strength, and toughness decreased with an increase in maximum temperature. The reduced stiffness, strength, Appl. Sci. 2018, 8, x FOR PEER REVIEW ◦ C, the stiffness, strength, and pre-peak toughness ratios are shown in Figure 6. At T = 200 and pre‐peak toughness ratios are shown in Figure 6. At Tmax = 200 °C, the stiffness, strength, and pre‐ max and toughness decreased with an increase in maximum temperature. The reduced stiffness, strength, and pre-peak toughness were not reduced significantly. However, when Tmax reached to 400 ◦ C, peak toughness were not reduced significantly. However, when T max reached to 400 °C, the stiffness, and pre‐peak toughness ratios are shown in Figure 6. At T max = 200 °C, the stiffness, strength, and pre‐ the stiffness, strength, pre-peak toughness were reduced significantly. The reduced stiffness, strength, and pre‐peak and toughness were reduced significantly. The reduced stiffness, strength, and peak toughness were not reduced significantly. However, when T max reached to 400 °C, the stiffness, ◦ strength, and pre-peak toughness ratios are 51%, 48%, and 36%, respectively. At T = 600 C, pre‐peak toughness ratios are 51%, 48%, and 36%, respectively. At Tmax = 600 °C, the reduced stiffness, max strength, and pre‐peak toughness were reduced significantly. The reduced stiffness, strength, and the reduced stiffness, strength, and pre-peak toughness ratios reached 79%, 71%, and 61%, respectively. strength, and pre‐peak toughness ratios reached 79%, 71%, and 61%, respectively. When T max = 800 pre‐peak toughness ratios are 51%, 48%, and 36%, respectively. At Tmax = 600 °C, the reduced stiffness, When Tmax = 800 ◦ C, the material lost its strength. °C, the material lost its strength. strength, and pre‐peak toughness ratios reached 79%, 71%, and 61%, respectively. When Tmax = 800 °C, the material lost its strength.
Figure 5. Completed uniaxial compressive curves for concrete subjected to various temperatures. Figure 5. Completed uniaxial compressive curves for concrete subjected to various temperatures. Figure 5. Completed uniaxial compressive curves for concrete subjected to various temperatures.
Figure 6. Maximum temperature effect on stiffness, strength, and pre‐peak toughness. Figure 6. Maximum temperature effect on stiffness, strength, and pre-peak toughness.
As the heat temperature increases, cracks are formed at the particle and cementitious material As theFigure 6. Maximum temperature effect on stiffness, strength, and pre‐peak toughness. heat temperature increases, cracks are formed at the particle and cementitious material interface and also the C‐S‐H (calcium‐silicate‐hydrate) colloid, provided that the cementitious interface and also the C-S-H (calcium-silicate-hydrate) colloid, provided that the cementitious material material strength is initially decomposed at a 200 °C temperature. When the elevated temperature As the heat temperature increases, cracks are formed at the particle and cementitious material strength is initially decomposed atcalcium a 200 ◦ Chydroxide temperature. When the calcium elevatedcarbonate temperature reaches reaches 500 °C and 600 °C, (Ca(OH) 2) and (CaCO 3) interface and also the C‐S‐H (calcium‐silicate‐hydrate) colloid, provided that the cementitious ◦ ◦ 500 Cdecomposition occurs [16]. This is why the stiffness and strength decrease as the heat temperature is and 600 C, calcium hydroxide (Ca(OH)2 ) and calcium carbonate (CaCO3 ) decomposition material strength is initially decomposed at a 200 °C temperature. When the elevated temperature occursincreased. As the stiffness and strength decrease, the toughness with respect to the area under the [16]. This is why the stiffness and strength decrease as the heat temperature is increased. As the reaches 500 °C and 600 °C, calcium hydroxide (Ca(OH) 2) and calcium carbonate (CaCO3) stress‐strain curve is also decreased, as shown in Figure 5. the stiffness and strength decrease, the toughness with respect to area under the stress-strain curve is decomposition occurs [16]. This is why the stiffness and strength decrease as the heat temperature is For the also decreased, as post‐peak shown in behavior, Figure 5. two classified post‐peak behavior fracture types for rock: snap increased. As the stiffness and strength decrease, the toughness with respect to the area under the through (Class I) and snap back (Class II), were defined using uniaxial compressive tests, as shown For the post-peak behavior, two classified post-peak behavior fracture types for rock: snap through in Figure 7 (Wawersik 1968). In Class I (stable fracture type), the post‐peak cumulative strain energy stress‐strain curve is also decreased, as shown in Figure 5. (Class I) and snap back (Class II), were defined using uniaxial compressive tests, as shown in Figure 7 cannot sustain crack propagation. The specimen carrying decreases the for increase For the post‐peak behavior, two classified post‐peak capacity behavior fracture with types rock: in snap (Wawersik 1968). In Class I (stable fracture type), the post-peak cumulative strain energy cannot sustain strain. In Class II (unstable fracture type), the post‐peak strain or degree of specimen displacement through (Class I) and snap back (Class II), were defined using uniaxial compressive tests, as shown
crack propagation. The specimen carrying capacity decreases with the increase in strain. In Class II in Figure 7 (Wawersik 1968). In Class I (stable fracture type), the post‐peak cumulative strain energy (unstable fracture type), the post-peak strain or degree of specimen displacement decreases; however, cannot sustain crack propagation. The specimen carrying capacity decreases with the increase in strain. In Class II (unstable fracture type), the post‐peak strain or degree of specimen displacement
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decreases; however, the energy stored in the specimen can sustain crack propagation, even without decreases; however, the energy stored in the specimen can sustain crack propagation, even without external pressure. Figure 5 shows that concrete suffers from heat‐driven damage, causing the post‐ the energy stored in the specimen can sustain crack propagation, even without external pressure. external pressure. Figure 5 shows that concrete suffers from heat‐driven damage, causing the post‐ peak stability from snap back (Class II) convert to snap through (Class I), and the transition Figurestability 5 showsfrom that concrete suffers from causing the post-peak stability from peak snap back (Class II) heat-driven convert to damage, snap through (Class I), and the transition temperature is between 200 to 400 °C. Therefore, the post‐peak toughness increased at T max = 200−500 snap back (Class II) convert to snap through (Class I), and the transition temperature is between 200 to temperature is between 200 to 400 °C. Therefore, the post‐peak toughness increased at T max = 200−500 °C (Figure 8). 400 ◦ C. Therefore, the post-peak toughness increased at Tmax = 200−500 ◦ C (Figure 8). °C (Figure 8).
stress stress
A A
B B
CLASS II CLASS II
CLASS I CLASS I C C
D D strain strain
E E
Figure 7. Rock failure behavior classification in uniaxial compression adapted from Wawersik (1968). Figure 7. Rock failure behavior classification in uniaxial compression adapted from Wawersik (1968). Figure 7. Rock failure behavior classification in uniaxial compression adapted from Wawersik (1968).
Figure 8. Maximum temperature effect on post-peak toughness. Figure 8. Maximum temperature effect on post‐peak toughness. Figure 8. Maximum temperature effect on post‐peak toughness.
5.1.2. Damage Characteristics Examined Using the Wave Velocity Ratio 5.1.2. Damage Characteristics Examined Using the Wave Velocity Ratio 5.1.2. Damage Characteristics Examined Using the Wave Velocity Ratio Naus [22] [22] indicated that when concrete is subjected heat damage, the Poisson’s ratio of Naus indicated that when concrete is subjected to heatto damage, the Poisson’s ratio of concrete Naus [22] indicated that when concrete is subjected to heat damage, the Poisson’s ratio concrete will decrease with an increase in maximum temperature. This is because the heat‐damage will decrease with an increase in maximum temperature. This is because the heat-damage resulting of in concrete will decrease with an increase in maximum temperature. This is because the heat‐damage resulting in concrete cracks increases decrease the lateral According deformation. According to the wave shear‐ concrete cracks increases to decrease theto lateral deformation. to the shear-pressure resulting in concrete increases to decrease lateral deformation. According to the shear‐ pressure wave velocity ratio (V /Vp) relation to Poisson’s ratio, as shown in Equation (2) [15], V s/Vp velocity ratio (Vs /Vp )cracks relation to sPoisson’s ratio, asthe shown in Equation (2) [15], Vs /Vp increases with pressure wave velocity ratio (V p) relation to Poisson’s ratio, as shown in Equation (2) [15], Vs/Vp aincreases with a decreasing Poisson’s ratio. In other words, as the maximum temperature increases, decreasing Poisson’s ratio. Ins/V other words, as the maximum temperature increases, the measured increases with a decreasing Poisson’s ratio. In other words, as the maximum temperature increases, Vs/V p of concrete should UP be measurement increased. UP was measurement used in this study Vthe should be increased. used in thiswas study to measure the Vto s /Vmeasured p of concrete s the measured V s/Vp of concrete should be increased. UP measurement was used in this study to s and V p of concrete after heat treatment. The normalized V s/Vp was then calculated. measure the V and Vp of concrete after heat treatment. The normalized Vs /Vp was then calculated. measure the Vs and Vp of concrete after heat treatment. The normalized Vs/Vp was then calculated. s (2) Vs (1 − 2ν), , , (2) = (2) Vp 2(1 − υ) where ν is the Poisson’s ratio. where Figure ν is the Poisson’s ratio. shows the Vs/V p relation to maximum temperature and reduced ratio of stiffness, where ν is the9 Poisson’s ratio. Figure 9 shows the Vs/Vp relation to smaximum temperature and reduced ratio of stiffness, strength, and toughness. As expected, as V p increased with an increase in maximum temperature, Figure 9 shows the Vs /Vp relation to /V maximum temperature and reduced ratio of stiffness, strength, and toughness. As expected, as V s/Vp increased with an increase in maximum temperature, as shown in Figure 9a, the V s /V p also increased with an increase in the in reduced ratio of stiffness, strength, and toughness. As expected, as Vs /Vp increased with an increase maximum temperature, as shown in Figure 9a, the Vs/Vp also increased with an increase in the reduced ratio of stiffness,
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reduced ratio of stiffness (%)
maximum temperature (℃)
as shown in Figure 9a, the Vs /Vp also increased with an increase in the reduced ratio of stiffness, strength, and and toughness, toughness, as as shown −d. It It is is interesting interesting to has a a high high strength, shown in in Figure Figure 9b 9b−d. to note note that that VVSS/V /VPP has positive correlation with the maximum temperature and the reduced ratio of stiffness, strength, positive correlation with the maximum temperature and the reduced ratio of stiffness, strength, and 2 value) between V /V and maximum temperature, 2 value) and toughness. coefficient of determination toughness. The The coefficient of determination (R(R between VS/V maximum temperature, S P and P reduced ratio of stiffness, strength, and toughness reached 0.82, 0.94, 0.93, and 0.88, respectively. reduced ratio of stiffness, strength, and toughness reached 0.82, 0.94, 0.93, and 0.88, respectively. This This suggests that VS /VP could be applied to identify the degree of thermal-induced damage in suggests that V S/VP could be applied to identify the degree of thermal‐induced damage in concrete concrete based on the experimental result in Figure 9. based on the experimental result in Figure 9.
400
800
y = 6319.9x2 - 6194.4x + 1595.5 R² = 0.8235
1200 0.30
0.40
0.50
0.60
0.70
0.80
Vs/Vp
0.90
-40 0 40 80
y = 584.88x2 - 497.63x + 105.8 R² = 0.9366
120 0.30
0.40
0.50
-40 0 40
120 0.30
y = 594.42x2 - 519.33x + 112.26 R² = 0.9305 0.40
0.50
0.60
Vs/Vp
(c)
0.70
0.80
0.90
(b) reduced ratio of toughness(%)
reduced ratio of strength (%)
(a)
80
0.60
Vs/Vp
0.70
0.80
0.90
-40 0 40 80 120 0.30
y = 495.49x2 - 421x + 86.491 R² = 0.8831 0.50
0.70
0.90
Vs/Vp
(d)
Figure 9. Correlation of wave velocity ratio (V S/VP) to reduced ratio of (a) maximum temperature; (b) Figure 9. Correlation of wave velocity ratio (V S /VP ) to reduced ratio of (a) maximum temperature; stiffness; (c) strength; (d) and pre‐peak toughness, respectively. (b) stiffness; (c) strength; (d) and pre-peak toughness, respectively.
5.2. Artificial Intelligence Analysis 5.2. Artificial Intelligence Analysis Artificial Intelligence was used to identify the experimental findings. In machine learning Artificial Intelligence was used to identify the experimental findings. In machine learning processing, 21 experimental data records were used. Table 2 shows the correlation coefficient processing, 21 experimental data records were used. Table 2 shows the correlation coefficient between between the input and output variables. The correlation coefficient in Table 2 was used to examine the input and output variables. The correlation coefficient in Table 2 was used to examine the degree the degree of correlation between input and output variables. An absolute value of the coefficient of correlation between input and output variables. An absolute value of the coefficient greater than or greater than or equal to 0.8 represents high correlation; whereas a value smaller than 0.8 but greater equal to 0.8 represents high correlation; whereas a value smaller than 0.8 but greater than or equal to than or equal to 0.7 shows medium‐high correlation. Therefore, the maximum temperature with high 0.7 shows medium-high correlation. Therefore, the maximum temperature with high or medium-high or medium‐high correlation to Vs/Vp, stiffness, pre‐peak toughness, and post‐peak toughness was correlation to Vs /Vp , stiffness, pre-peak toughness, and post-peak toughness was obtained. obtained. An Artificial Neural Network (ANN) was built using WEKA. The inputs of the ANN include An Artificial Neural Network (ANN) was built using WEKA. The inputs of the ANN include the aforementioned variables, and the output is the maximum temperature. For prediction, the mean the aforementioned variables, and the output is the maximum temperature. For prediction, the mean absolute error is 99.2042, and the root-mean-square error is 130.3246. A high correlation coefficient absolute error is 99.2042, and the root‐mean‐square error is 130.3246. A high correlation coefficient of of 0.8646 between the real maximum temperature and the one predicted by the ANN model is 0.8646 between the real maximum temperature and the one predicted by the ANN model is obtained. obtained. High correlation means high covariance between the two temperatures, and high covariance High correlation means high covariance between the two temperatures, and high covariance means means that the model describes how the predicted maximum temperature varies or changes with that the model describes how the predicted maximum temperature varies or changes with the real the real maximum temperature well. Therefore, the maximum temperature can be predicted using maximum temperature well. Therefore, the maximum temperature can be predicted using the the established ANN model. These results show that the fire-damage temperature could be simply established ANN model. These results show that the fire‐damage temperature could be simply detected by Vs/Vp. Note that the rate of heating is only related to the strength with a coefficient of 0.19. However, more data records are needed to confirm this finding. Table 3 shows the correlation
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detected by Vs /Vp . Note that the rate of heating is only related to the strength with a coefficient of 0.19. However, more data records are needed to confirm this finding. Table 3 shows the correlation coefficient between the output variables. The Vs /Vp has a high correlation with stiffness, strength, and pre-peak toughness, as shown in Table 3. These analysis results show a good agreement with those from the experiment. Table 2. Correlation coefficient between input and output variables. Output Variables
Input Variables Wave Velocity Ratio
Stiffness
Strength
Pre-Peak Toughness
Post-Peak Toughness
−0.14 0.78 0.17
0.23 −0.87 −0.42
0.49 0.19 −0.65 −0.41
0.37 −0.7 −0.4
0.24 −0.73 −0.22
designed strength rate of heating maximum temperature exposure time
Table 3. Correlation coefficient between output variables. Output Variables
Stiffness
Strength
Pre-Peak Toughness
Post-Peak Toughness
wave velocity ratio stiffness strength pre-peak toughness
−0.91
−0.89 0.99
−0.84 0.94 0.97
−0.67 0.8 0.84 0.9
6. Conclusions This study was the first to apply ultrasonic pulse measurement to investigate the thermal-induced damage characteristics of concrete by conducting the uniaxial compressive test after concrete was subjected to heating under different thermal conditions (the rate of heating, maximum temperature, exposure time, as well as cooling condition). During the uniaxial compressive test process, a circumferential extensometer was used to prevent unstable crack growth. A complete stress-strain curve was then obtained. The experimental results show that the stiffness, strength, and pre-peak toughness decrease with an increase in maximum temperature, whereas the post-peak toughness increased with an increase in maximum temperature at a maximum temperature ranging from 200 to 500 ◦ C. In addition, the wave velocity ratios in relation to the maximum temperature, stiffness, strength, and pre-peak toughness were obtained. The obtained experimental data was then analyzed using artificial intelligence. The thermal conditions in the experiment were used as input variables. The experimental stiffness, strength, toughness, and wave velocity ratio results were used as output variables. The obtained correlation of the input and output variables using artificial intelligence showed that the wave velocity ratio has a high relation with the maximum temperature, stiffness, strength, and pre-peak toughness. These results indicate that the wave velocity ratio can be used to identify the thermal-induced damage characteristics. Ultrasonic pulse measurement coupled with the uniaxial compressive test can be employed to investigate the degree of damage with respect to the maximum temperature of concrete subject to heating. Author Contributions: Conceptualization, L.-H.C. and Y.-C.C.; Methodology, L.-H.C.; Software, K.-W.H.; Visualization, W.-C.C. and H.-J.L.; Formal Analysis, W.-C.C., H.-J.L., and K.-W.H.; Validation, M.-Y.L. and T.-C.W.; Resources, C.-F.C.; Writing-Original Draft Preparation, W.-C.C.; Writing-Review & Editing, W.-C.C.; Supervision, L.-H.C.; Project Administration, H.-J.L. Funding: This research was funded by Architecture and Building Research Institute, Ministry of the Interior, ROC (Taiwan) grant number 105301070000G0032. Acknowledgments: This research is supported by the Architecture and Building Research Institute, Ministry of the Interior, ROC (Taiwan). Conflicts of Interest: The authors declare no conflict of interest.
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