Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System
Abstract
:1. Introduction and Related Works
2. Construction of Intelligent Automatic Blasting Vibration Monitoring System
2.1. Configuration of the Data Acquisition Device
2.2. Neural Network Model Based on the Fuzzy-Rough Set
- (1)
- The fuzzification of excavated and unexcavated rock’s integrity
- (2)
- The fuzzification of the relative position between the measuring point and the face
- (3)
- The fuzzification of the relative height of building floors
2.3. Development of Intelligent Data Analysis and Prediction Software
2.4. The Effectiveness of the Intelligent Automatic Blasting Vibration Monitoring System
3. Results and Discussions
3.1. Arrangement of Data Acquisition Device
- Step 1. Adjacent to the area of the foundation pit, the upper part is a mixed soil layer, the middle section is a soft rock formation, and the lower part is a hard rock formation. Sensors are placed on the surrounding rock in the foundation pit near the blasting point. A sensor is set every 5 m on the surrounding rock wall.
- Step 2. The ground near the palm surface is blasted, and four sensors are arranged on the ground in front of and behind the blasting palm with the same distance between each sensor.
- Step 3. High-rise building area, from −2 to 20 floors, each floor is equipped with a sensor, each measuring point is placed on the load-bearing wall of the room, each floor is set at the same location, and each sensor is kept on the same straight line.
3.2. Analysis of Monitoring Results of Near Foundation Pit
3.3. Analysis of Monitoring Results of the Hollow Effect
3.4. Analysis of Monitoring Results of High-Rise Buildings
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | Technical Description |
---|---|
Number of channels | X/Y/Z |
Frequency | 5–300 Hz |
Range (speed) | 0–25 cm/s |
Range (acceleration) | ±16 g |
Sample rate | 6.4 KSps |
Sample time | 1/2/5/10/15 s |
Storage capacity | 1 GB |
Measurement accuracy | 0.1 cm/s, 0.02 g |
Short-message warning | Support |
System error | <5% |
Working time | >72 H |
Stand-by time | >240 H |
Battery capacity | 6400 mAH |
Operating temperature | −20 °C to +70 °C |
Guideline of Attribute (Units) | Description of Decision | ||
---|---|---|---|
0 | 1 | 2 | |
a(kg) | <30 | 30~50 | >50 |
b(kg) | <200 | 200~300 | >300 |
c(m) | <20 | ≥20 | |
d(m) | <10 | 10~20 | >20 |
e(m) | <0.5 | ≥0.5 | |
f(ms) | <0.5 | 0.5~0.7 | >0.7 |
g | <0.5 | 0.5~0.7 | >0.7 |
h | 0~180 | >180 | |
i(m) | <0.3 | 0.3~0.8 | >0.8 |
X(cm/s) | <0.5 | 0.5~1 | >1 |
Y(cm/s) | <0.5 | 0.5~1 | >1 |
Z(cm/s) | <0.5 | 0.5~1 | >1 |
F(Hz) | <100 | 100~200 | >200 |
Location | Nci | X | Y | Z | F |
---|---|---|---|---|---|
R-Station | a | 35/100 | 15/100 | 22/100 | 32/100 |
b | 23/100 | 21/100 | 15/100 | 31/100 | |
c | 26/100 | 12/100 | 16/100 | 19/100 | |
d | 3/100 | 18/100 | 25/100 | 21/100 | |
e | 15/100 | 12/100 | 19/100 | 6/100 | |
f | 2/100 | 5/100 | 2/100 | 1/100 | |
g | 1/100 | 2/100 | 3/100 | 2/100 | |
h | 3/100 | 7/100 | 15/100 | 4/100 | |
i | 15/100 | 10/100 | 34/100 | 23/100 | |
Y-Station | a | 32/100 | 12/100 | 19/100 | 29/100 |
b | 20/100 | 18/100 | 13/100 | 23/100 | |
c | 21/100 | 17/100 | 11/100 | 24/100 | |
d | 15/100 | 18/100 | 25/100 | 21/100 | |
e | 15/100 | 12/100 | 19/100 | 6/100 | |
f | 12/100 | 18/100 | 16/100 | 21/100 | |
g | 25/100 | 29/100 | 32/100 | 25/100 | |
h | 10/100 | 2/100 | 8/100 | 13/100 | |
i | 0/100 | 0/100 | 0/100 | 0/100 | |
H-Station | a | 35/100 | 15/100 | 22/100 | 32/100 |
b | 23/100 | 21/100 | 15/100 | 31/100 | |
c | 26/100 | 12/100 | 16/100 | 19/100 | |
d | 3/100 | 18/100 | 25/100 | 21/100 | |
e | 15/100 | 12/100 | 19/100 | 6/100 | |
f | 4/100 | 9/100 | 1/100 | 0/100 | |
g | 5/100 | 6/100 | 9/100 | 1/100 | |
h | 21/100 | 15/100 | 18/100 | 28/100 | |
i | 0/100 | 0/100 | 0/100 | 0/100 |
Item | TC-4850 | TC-6850 |
---|---|---|
Shape | Square | Cylindrical |
Size | 168 mm × 99 mm × 64 mm | D = 81 mm, H = 80 mm |
Weight | 1 kg | 0.56 kg |
Integration | Separate sensor and host | Combined sensor and host |
Working temperature | −10~75 °C | −20~75 °C |
Sealing, waterproof and dustproof | Poor | Good |
Data transmission | USB | WIFI/4G/3G |
Power | Lithium battery | Lithium battery/Solar energy |
Operation | Wired | Wireless |
Data acquisition | Manual | Automatic |
Data output | PC | PC & Mobile phone |
Parameters output | Velocity | Velocity & Acceleration |
Data viewing | Paper report | Network data platform |
Sensor Number | Origin of the Age | Geotechnical Name | Layer Thickness/m |
---|---|---|---|
J1 | Q4ml | Plain fill | 1.85 |
J2 | Q4ml | Miscellaneous fill | 2.11 |
J3 | γ53 | Strongly weathered granite | 5.44 |
J4 | γ53 | Middle weathered granite | 3.69 |
J5 | γ53 | Slightly weathered granite | 7.15 |
J6 | γπ53 | Slightly weathered granite porphyry | 4.76/excavated |
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Chen, F.; He, G.; Dong, S.; Zhao, S.; Shi, L.; Liu, X.; Zhang, B.; Qi, N.; Deng, S.; Zhang, J. Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System. Appl. Sci. 2022, 12, 12. https://doi.org/10.3390/app12010012
Chen F, He G, Dong S, Zhao S, Shi L, Liu X, Zhang B, Qi N, Deng S, Zhang J. Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System. Applied Sciences. 2022; 12(1):12. https://doi.org/10.3390/app12010012
Chicago/Turabian StyleChen, Fan, Gengsheng He, Shun Dong, Shunjun Zhao, Lin Shi, Xian Liu, Baichuan Zhang, Ning Qi, Shenggui Deng, and Jin Zhang. 2022. "Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System" Applied Sciences 12, no. 1: 12. https://doi.org/10.3390/app12010012
APA StyleChen, F., He, G., Dong, S., Zhao, S., Shi, L., Liu, X., Zhang, B., Qi, N., Deng, S., & Zhang, J. (2022). Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System. Applied Sciences, 12(1), 12. https://doi.org/10.3390/app12010012