Predictive Maintenance · Real-time Analytics
Yunlu Technology needed to monitor structures across the full construction lifecycle, unify data from many stakeholders, and use that data to detect hazards, predict structural health, automate inspections, and identify damage before quality and safety incidents occur.
Reported impact
The platform helps Yunlu discover potential safety hazards faster, issue early warnings, support maintenance decisions, improve inspection efficiency, reduce construction errors, lower monitoring costs, and make structural damage analysis practical at whole-structure scale.
Yunlu Technology worked with Huawei Cloud to build a structural health monitoring big data IoT platform that spans construction and operations and maintenance. Field data is ingested from fiber-optic and other sensors through collection devices, on-site hosts, Huawei IoT modules, and Huawei edge devices, with Huawei edge computing modules processing data near the source so only higher-value data is sent upstream. In the cloud, all services run on Huawei cloud services with supporting data storage services and Huawei Cloud EI for analysis, identification, simulation, and early warning. Huawei blockchain platform is used to distribute blueprints and contracts so owners, planners, designers, construction teams, and O&M teams work from consistent records. ModelArts provides machine learning, deep learning, and image recognition for structural health prediction and photo-based inspection of grouting holes, while the EI platform accelerates mechanical simulation and structural damage identification across whole structures.
Monitoring data from fiber-optic and other sensors is collected by field devices and on-site hosts, then connected through Huawei IoT modules and Huawei edge devices. Huawei edge computing modules filter and process data locally before sending it to Huawei cloud services and storage. In the cloud, Huawei Cloud EI and ModelArts analyze environmental loads, structural response, and site images, then deliver predictions, damage identification, and early warnings to construction and O&M teams.
"And thanks to Huawei’s edge computing module, we have cut invalid data transmission by two-thirds and increased valid data by over 50 percent."
"Now we use Huawei Cloud’s ModelArts deep learning image recognition module to identify photographs of grouting holes and determine which are filled, which are not, and which are for threading pipes."
"Typically, you can upload 100 on-site images and achieve an effective identification rate of close to 80 percent. This solution has made the inspection department over 50 percent faster and reduced construction errors by 30 percent."
"Using Huawei Cloud’s EI platform, we have made mechanical simulations 100 times faster, and are able to implement an identification solution that supports real results from data, covers the whole structure, and includes monitoring and prediction."
"We leverage middleware, including data storage services, as well as various platforms, such as ModelArts’ machine learning, deep learning, and image recognition platforms."
"We leverage the Huawei blockchain platform to distribute all blueprints and contracts through the blockchain."
"We’ve deployed over 50 Huawei Cloud platform development modules. All the services run on Huawei cloud services."
"With edge, device, and cloud synergy, overall equipment costs have been reduced by over 70 percent and the system response is twice as fast."
"With the help of Huawei Cloud’s machine learning engineers, we’ve achieved a prediction accuracy of close to 90 percent."
"With the increasing interconnectivity of everything, we’ve adopted Huawei’s IoT modules to make it more convenient to access the system. We also use Huawei’s edge computing modules for prompt data processing."
"Yunlu Technology has worked with Huawei Cloud to build a world-leading structural health monitoring (SHM) big data IoT platform using new technologies that covers the entire construction lifecycle and improves construction safety."
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