Project Overview for the Deep Foundation Pit
Below, there will be a comprehensive solution and analysis of its advantages. For more product details, please refer to product page.
Inclino-Robot is much simpler to install than in-place inclinometer (IPI). The Shortage of in-place inclinometer (IPI) includes complex installation requiring the coordination of 3-4 workers, with installation and debugging of each borehole taking 3-5 hours. Advantages of the Inclino-Robot include installation and debugging that can be swiftly completed by only 1-2 personnel within an hour, significantly reducing labor and time costs.
How Much Does a Deep Foundation Pit Monitoring Cost?
Characteristics of In-place Inclinometer Costs:
Costs grow exponentially as the inclination depth increases.
Advantages of Inclino-Robot Costs:
Even in a 45-meter (147-feet) deep excavation, its costs remain substantially lower than an in-place inclinometer, offering high cost-effectiveness for dam water conservancy monitoring, slope monitoring, and various inclinometer applications during deep excavation projects. For detailed pricing information regarding geotechnical instrumentation for purchase or rental, or service fees for testing conducted on your behalf, kindly reach out to our info@geositter.com or visit our booth(s):2206 at IFCEE2024 on May7-10, 2024 and booth No.18 at the 30th Symposium of VGS on May 17, 2024.
How Do you Use an Inclinometer?
The advancement of Inclino-Robot is to ensure independent data recording for every 0.5 meters with the same testing principle as a manual inclinometer, thereby enhancing data accuracy but saving more costs compared with traditional manual measurement.
How to Efficiently Use Inclinometers for Precise Monitoring Data?
Inclino-Robot can be connected through two data transmission methods:
* Connect to the customer-specified platform
* Connect to the Inclino-robot Monitoring Cloud Platform.
Inclino-Robot integrates with the 51DataCloud Monitoring Platform, uploading and processing monitoring data in real time, ensuring data accuracy and reliability through advanced algorithms and models. Through platform data comparison, we found that the data results of the Inclino-Robot are highly consistent with those of the manual inclinometer.
Conclusion
In summary, the Inclino-Robot addresses the challenges of deep excavation monitoring by leveraging its advantages in labor cost savings, cost-effectiveness, enhancement of data accuracy. It successfully overcomes the shortcomings of traditional monitoring methods in complex deep excavation projects, providing an efficient, accurate, and economical automated monitoring solution for the project.