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The data collected by unmanned aerial vehicles (UAV) provide unique opportunities for applications, such as construction progress tracking and facility monitoring. To increase the effective...

The construction industry has long been plagued with late and nonpayments. Despite the desired benefits of payment automation and today’s increased access to digitized progress data, too of...

Supply chain integration remains an elusive goal for the construction and engineering industry. The high degree of fragmentation and the reliance on third-party financial institutions has pushed the physical and financial supply chains apart. The paper demonstrates how blockchain-based crypto assets (crypto currencies and crypto tokens) can address this limitation when used for conditioning the flow of funds based on the flow of products. The paper contrasts the integration between cash and product flows in supply chains that rely on fiat currencies and crypto assets for their payment settlement. Two facets of crypto asset-enabled integration, atomicity and granularity, are further introduced. The thesis is validated in the context of construction progress payments. The as-built data captured by unmanned aerial and ground vehicles was passed to an autonomous smart contract-based method that utilizes crypto-currencies and crypto tokens for payment settlement; the resulting payment datasets, written to the Ethereum blockchain, were analyzed in terms of their integration of product and cash flow. The work is concluded with a discussion of findings and their implications for the industry.

This paper introduces an autonomous payment administration solution, integrating blockchain-enabled smart contracts and robotic reality capture technologies. The construction progress is captured, analyzed, and documented respectively using sensing, machine intelligence, and as-built building information models (BIM). The progress data is stored in a distributed manner using content addressable file sharing; it is then broadcasted to a smart contract which administers payments and transfers lien rights respectively using crypto currencies/tokens and non-fungible tokens (NFT). The method was successfully implemented for payments to 7 subcontractors in two real-world commercial construction projects in Canada and United States where progress was captured using a camera-equipped unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) equipped with a laser scanner. The method eliminates reliance on today's heavily intermediated payment applications and showed promise for achieving accurate, efficient, and timely payment administration. Future work should further explore the connections between off- and on-chain realities.

This paper presents a computer vision-based algorithm that automatically detects the components of an interior partition and infers its current state using 2D digital images. The algorithm relies on four integrated shape and color-based modules, which detect studs, insulation, electrical outlets, and three states for drywall sheets (installed, plastered, and painted). Based on the results of the four modules, images are classified into five states. The proposed method was validated using three image databases of indoor construction sites captured by a quadcopter (a type of unmanned aerial vehicle), a smartphone, and collected from publically available sources on the internet. The method's high accuracy rates, its fast performance, and applicability to different contexts such as automated robotic inspection are indicative of its promising performance. The visual detection results can potentially provide situational awareness for construction trades, provide future progress tracking systems with information on actual state, and help leverage the use of image processing at indoor sites.