The construction industry continues to be a hotbed of innovation, with activity driven by an increased focus on environmental sustainability and workplace safety, and the growing importance of technologies such as the Internet-of-Things (IoT) and robotics. In the last three years alone, there have been over 248,000 patents filed and granted in the construction industry, according to GlobalData’s report on Artificial intelligence in Construction: Excavator payload estimators. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
80+ innovations will shape the construction industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the construction industry using innovation intensity models built on over 179,000 patents, there are 80+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, excavator payload estimators, and robotic water surface cleaner are disruptive technologies that are in the early stages of application and should be tracked closely. Intelligent motor controllers, sensor-integrated door wings, and autonomous worksite control machines are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are hydraulic excavator controls and excavator performance monitor, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the construction industry
Excavator payload estimators is a key innovation area in artificial intelligence
Excavator payload estimators use artificial intelligence to estimate themaximum load that an excavating machine can handle. Using data on the operational status of the machine and its capacities, the system can calculate a maximum payload and inform the operator when the excavator is over- or under-loaded.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 10 companies, spanning technology vendors, established construction companies, and up-and-coming start-ups engaged in the development and application of excavator payload estimators.
Key players in excavator payload estimators – a disruptive innovation in the construction industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to excavator payload estimators
|Company||Total patents (2010 - 2022)||Premium intelligence on the world's largest companies|
|Hitachi||55||Unlock Company Profile|
|Caterpillar||37||Unlock Company Profile|
|Komatsu||26||Unlock Company Profile|
|CR||19||Unlock Company Profile|
|FLANDERS ELECTRIC MOTOR SERVICE||13||Unlock Company Profile|
|Deere & Co||10||Unlock Company Profile|
|CNH Industrial||10||Unlock Company Profile|
|Sumitomo Heavy Industries||9||Unlock Company Profile|
|Aito Capital||7||Unlock Company Profile|
|Doosan Infracore||5||Unlock Company Profile|
Source: GlobalData Patent Analytics
A leading company in the excavator payload estimators space is Hitachi, which is a leading provider of excavation machinery. A recent innovation by Hitachi allows excavators to be fitted with a controller, which can compute the excavator’s payload using information on the material and the machine and inform the operator when the excavator is overloaded. The controller comprises a storage device that stores a load overflow reference value stipulated by the interrelationship of a load value for an object being worked, the orientation of a work machine, and the operation status of the work machine. Other leading companies in the space include Caterpillar and Komatsu.
To further understand the key themes and technologies disrupting the construction industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Construction.