Periodic Reporting for period 1 - Quakebots (Artificial Intelligence and IoT for seismic monitoring)
Berichtszeitraum: 2018-04-01 bis 2018-09-30
QUAKEBOTS is an innovative distributed IoT neural network for the real-time monitoring of the dynamic response of buildings and structures. The purpose of QUAKEBOTS is to help owners and managers of building stock to monitor the health and safety of their assets by analyzing vibrations and applying machine learning algorithms to infer the main parameters characterizing the building’s own response to stimuli. The system provides the customer with a tool for early assessment of structures status after any earthquake event, continuous reporting of induced vibration and solicitations to infrastructures and buildings, predictive maintenance of infrastructures and buildings and assessing seismic induced damage and ultimately increase safety thanks to early warning and disaster management services offered by its platform.
The objective of the project is to validate the technology behind the QUAKEBOTS system through an extensive piloting on the field with a large number of installations, and furtherly improve the algorithms for dynamic response modeling of buildings and early warning in case of earthquakes for emergency management.
The objective of the project is to validate the technology behind the QUAKEBOTS system through an extensive piloting on the field with a large number of installations, and furtherly improve the algorithms for dynamic response modeling of buildings and early warning in case of earthquakes for emergency management.
Through the SME-Instrument Phase 1 project, thanks to the funding received, we were able to:
a. assess the system's precision and output validity against golden standards in the Structural Health Monitoring scientific research, with the aid of the academic partner Università de L’Aquila ,
b. install 70 new units, reaching a total of 400 devices on the field, for data collection and field testing of the system MVP, validating the system architecture,
c. achieve the final industrial design for the QUAKEBOTS MVP, consolidate the supply chain, define a final product BOM and creating a final assembly diagram for the different product versions;
d. define of a go-to-market strategy, development of a 5-year Profit&Loss forecast and SWOT analysis;
e. define the business model for a data/platform Business, quantifying the final license and manufacturing costs to maintain the business at full operations;
f. define of a work plan and budget for the Phase 2 (PH2) project;
During the Phase 1 project, we received as part of Phase 1 ancillary services granted, a mentorship and coaching by Ms Kaija Pöysti.
We foresee an application for a SME-Instrument Phase 2 project of 2 years as a follow-up to the feasibility study, with a total budget of 1.470.375€
a. assess the system's precision and output validity against golden standards in the Structural Health Monitoring scientific research, with the aid of the academic partner Università de L’Aquila ,
b. install 70 new units, reaching a total of 400 devices on the field, for data collection and field testing of the system MVP, validating the system architecture,
c. achieve the final industrial design for the QUAKEBOTS MVP, consolidate the supply chain, define a final product BOM and creating a final assembly diagram for the different product versions;
d. define of a go-to-market strategy, development of a 5-year Profit&Loss forecast and SWOT analysis;
e. define the business model for a data/platform Business, quantifying the final license and manufacturing costs to maintain the business at full operations;
f. define of a work plan and budget for the Phase 2 (PH2) project;
During the Phase 1 project, we received as part of Phase 1 ancillary services granted, a mentorship and coaching by Ms Kaija Pöysti.
We foresee an application for a SME-Instrument Phase 2 project of 2 years as a follow-up to the feasibility study, with a total budget of 1.470.375€
We expect that the QUAKEBOTS system, thanks to its novel approach based on Machine Learning and IoT technologies, could bring a significant step forward for the overall Structural Health Monitoring practice worldwide.
Through our field trials, we demonstrated how our technology is capable to:
a. perform a complete assessment of the Health Status of a Building in a completely autonomous way, minimizing the need for human intervention, and increasing the chances to detect defects and anomalies
b. be able to detect a set of conditions to predict the happening of a seismic event, allowing for some anticipation before the actual destructive wave coming. Through our peer-to-peer network, we were able to communicate the event to neighboring nodes, anticipating the mechanical wave
c. be able to map the terrain, allowing for a more resolute mapping of the seismic zoning
d. assess potential damage to valuable asset and industrial machinery
Through our field trials, we demonstrated how our technology is capable to:
a. perform a complete assessment of the Health Status of a Building in a completely autonomous way, minimizing the need for human intervention, and increasing the chances to detect defects and anomalies
b. be able to detect a set of conditions to predict the happening of a seismic event, allowing for some anticipation before the actual destructive wave coming. Through our peer-to-peer network, we were able to communicate the event to neighboring nodes, anticipating the mechanical wave
c. be able to map the terrain, allowing for a more resolute mapping of the seismic zoning
d. assess potential damage to valuable asset and industrial machinery