Source-linked AI summary
Construction Payment Automation Using Blockchain-Enabled Smart Contracts and Reality Capture Technologies
Hesam Hamledari, Martin Fischer
TL;DR
Construction payment workflows remain manual and intermediated despite digitized progress data, leaving progress-to-payment automation unresolved. The paper develops a blockchain smart-contract system that uses distributed reality-capture data for on-chain payment settlement and demonstrates feasible processing across two commercial projects and seven subcontractors.
Problem
Existing payment applications do not reliably automate the transition from job-site progress observations to construction payments or support on-site reality capture.
Method
The paper uses distributed content-addressable progress data, blockchain, and smart contracts to condition and automate construction payment settlements.
Results
The method successfully processed payments to seven subcontractors in two commercial construction projects and demonstrated feasible payment processing.
Takeaways & Limitations
The results show promise for increasing the frequency, granularity, and transparency of construction payments.
Takeaways & Limitations
Manually reported progress can jeopardize the reliability of the payment process.
Abstract
from arXiv · showhide
This paper presents a smart contract-based solution for autonomous administration of construction progress payments. It bridges the gap between payments (cash flow) and the progress assessments at job sites (product flow) enabled by reality capture technologies and building information modeling (BIM). The approach eliminates the reliance on the centralized and heavily intermediated mechanisms of existing payment applications. The construction progress is stored in a distributed manner using content addressable file sharing; it is broadcasted to a smart contract which automates the on-chain payment settlements and the transfer of lien rights. The method was successfully used for processing payments to 7 subcontractors in two commercial construction projects where progress monitoring was performed using a camera-equipped unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) equipped with a laser scanner. The results show promise for the method's potential for increasing the frequency, granularity, and transparency of payments. The paper is concluded with a discussion of implications for project management, introducing a new model of project as a singleton state machine.
STANFORD UNIVERSITY
The paper is titled “Construction Payment Automation Using Blockchain-Enabled Smart Contracts and Reality Capture Technologies” and is authored by Hesam Hamledari and Martin Fischer.
- The paper title identifies construction payment automation as its subject.
- The title links blockchain-enabled smart contracts with reality capture technologies.
- Hesam Hamledari and Martin Fischer are listed as the authors.
1 INTRODUCTION
The introduction frames construction payment as a critical cash-flow process whose existing manual, intermediated workflows do not reliably convert job-site progress into payments. The paper proposes connecting reality-captured progress data with decentralized smart-contract settlements and evaluates the approach in two commercial projects.
- Cash flow management is crucial to the financial wellbeing of construction and engineering firms.
- Digitized progress data from BIM and reality capture could streamline payments and automate the transition from product flow to cash flow.
- Current construction payment workflows are slow, information intensive, manual, heavily intermediated, and unable to use reality-capture data for automation.
- The proposed solution automates and decentralizes construction payments conditioned on progress assessments from on-site reality capture.
- The method combines content-addressable distributed data sharing, blockchain immutability, and smart-contract self-enforceability.
- The solution was used to process payments to subcontractors in two commercial construction projects documented with robotic reality capture.
2 BACKGROUND
Background studies present blockchain and smart contracts as tools for transparency, traceability, governance, and payment administration, while repeatedly identifying limited real-world validation. Payment-focused work particularly highlights centralized control, manual intermediation, and weak execution guarantees as unresolved concerns.
- Blockchain research in AEC emphasizes transparency, traceability, immutable records, and improved governance through smart-contract self-execution.
- Several proposed blockchain and smart-contract frameworks require validation in real-world projects or specific domains.
- A blockchain-based case study across 44 tasks and 12 wastewater treatment plant projects reduced work hours by 49%.
- Prior payment research associates smart contracts with improved capital flow, reduced contract-processing costs, historical stakeholder data, and greater payment transparency.
- The paper identifies a need for smart contracts that automate progress-to-payment transitions while incorporating on-site reality capture and real-world implementation.
3 METHOD: AUTONOMOUS CONSTRUCTION PROGRESS PAYMENTS
The method links construction-site product flow to cash flow through reality capture, content-addressable sharing, and blockchain smart contracts. It replaces centralized payment workflows with decentralized documentation and automated settlement conditioned on progress data.
- Construction progress payments are modeled as transitions from physical product flow to social cash flow.
- Existing centralized payment automation remains vulnerable to single points of failure, data rewriting, denial of service, and information asymmetry.
- Blockchain smart contracts decentralize payment administration by executing contractual terms and reaching consensus on transactions, accounts payable, and accounts receivable.
- Reality capture supplies an oracle linking blockchain state transitions to physical events and makes work valuation less dependent on individual observers.
- IPFS stores progress information by content rather than location, creating immutable links between physical reality and public-blockchain records.
- The integrated system uses reality capture, IPFS, smart contracts, and public ledgers to translate job-site progress into payments without invoice collection and payment certification workflows.
- The approach links off-chain product-flow records to on-chain cash-flow records through content identifiers, valuations, transactions, lien tokens, and payee keys.
- The smart contract autonomously settles payments and uses ERC-721 LIEN tokens to represent and transfer lien rights alongside payments.
4 EXPERIMENTAL SETUP AND RESULTS
The experimental setup deployed the smart contract payment system on two commercial construction projects and evaluated it under differing reality-capture configurations and progress-data granularities.
- Deployment: The proposed Solidity v0.6.2 solution processed progress payments on two commercial construction sites.Transactions used an Ethereum test network and a private IPFS network among project participants.
- Project settings: Project A was located in Ontario and collected data over 4 weeks, while Project B was located in California and collected data over 5 months.Table 1 describes the projects, payment-processed trades, and building elements covered by the reality captures.
- Reality capture: Project A used a camera-equipped UAV to collect digital images, whereas Project B used a laser scanner mounted on a UGV.The two projects therefore tested different reality-capture sensors and data sources.
- Test design: The tests were designed to evaluate the feasibility and performance of the smart contract-based system across different project settings.They differed in reality-capture solutions, sensors, analytics tools, and data formats.
- Test design: Test 1 provided granular progress data for 104 inspected building elements connected to BIM, while Test 2 provided aggregate percentage completion.These disparities were intended to evaluate generalizability and applicability to common progress-monitoring practices.
1 A UAV (Fig. 7a)
The supplied passage identifies an outside vendor and lists .rvt, .nwd, and XML formats.
- A UAV: An outside vendor is identified in the supplied setup information.
- A UAV: The listed file formats include .rvt and .nwd.
- A UAV: XML is also listed among the supplied data formats.
2 B UGV (Fig. 7c)
The tests connected reality-captured progress data to distributed storage and smart-contract payment processing, while revealing differences in automation, accuracy assessment, and processing time.
- Reality capture and BIM: Test 1 used automated computer vision on UAV photographs to generate progress states for indoor partitions.The resulting progress data was incorporated into industry foundation classes-based 4D BIMs using an automated method.
- Reality capture and BIM: Test 2 used UGV-captured point clouds processed by an outside vendor whose algorithms and automation level were unknown to the authors.The process was believed to require significant human involvement, and as-built BIMs were not created.
- Payment automation: Progress data, as-built BIMs when available, and supporting valuation files were uploaded to private IPFS, with content identifiers sent to the smart contract through an RPC module.The smart contract operation did not involve human involvement.
- Accuracy and limitations: Reality-capture accuracy was unavailable for Test 2 because the vendor neither manually verified site conditions nor publicly shared platform accuracy.Remote-sensing accuracy was defined by whether reported as-built conditions matched manually verified on-site conditions.
- Results: All transactions were manually verified, and no issues were observed with the smart contract’s payment settlement.Payments were successfully settled for the entire scope of work covered in the progress reports.
- Processing time: Processing time for reality capture varied with the sensor and tracking solution, with point-cloud analysis more time-consuming than digital-image analysis.Reported processing times excluded data collection and data preparation.
5 DISCUSSION
The proposed method connects physical job-site observations to payment processing through decentralized, automated smart contracts. Its discussion highlights faster, more transparent payments while retaining human roles and depending on the accuracy, granularity, and timing of reality-capture data.
- Contribution: The method bridges physical job-site observations and social-reality progress payments without relying on centralized intermediaries.It decentralizes stages linking captured site conditions to payment workflows.
- Operational boundary: Full decentralization is limited because not all payment-processing steps can be decentralized, and competing business objectives may constrain its feasibility.The discussion therefore distinguishes decentralized payment execution from complete decentralization of the workflow.
- Decentralization and human involvement: The smart contract enables decentralized execution of contractual agreements and automates payment processing without a server-customer model.Human involvement remains necessary for preparing inputs and analyzing outputs, particularly for on-site activities.
- Operational boundary: Human involvement can improve on-site data quality and permit timely adjustments when capture is faulty or equipment malfunctions.Thus, automating steps does not necessarily increase payment-automation reliability by itself.
- Accuracy and payment effects: Reality-capture accuracy is critical: misclassification affected 5% of building elements in one test and delayed or accelerated the associated payments.The discrepancy affected payment timing rather than the total valuation of the work.
- Granularity and scope: Payment granularity and scope depend on the reality-capture and progress-reporting format, ranging from building-element payments to cost-code reporting.Element-level processing was possible in the first test but not in the second because of the outside vendor’s reporting format.
- Payment timing: Payment-cycle duration is constrained by reality-capture practice rather than smart-contract processing, with technical processing possible within a few days after site observations.The full process also includes robotic data capture and data preparation times.
- Transparency and verifiability: The method increases payment transparency by making payment status, completed workflows, prior transactions, and linked input data directly verifiable without gatekeepers.Off-chain storage and on-chain references connect transactions with captured site data, analysis scripts, and valuation schedules.
6 CONCLUSION
The paper proposes integrating reality-capture and BIM-based progress data with decentralized smart contracts to automate construction payments. Validation across two commercial projects demonstrated feasible payment processing, while adoption remains constrained by early-stage technology and unresolved standardization, verification, data-fusion, and settlement needs.
- Smart contracts address manual, heavily intermediated payment workflows through decentralization and guaranteed execution.
- A major adoption barrier is the missing link between on-chain payment settlement and off-chain physical construction progress.
- The proposed solution connects reality-capture and BIM-enabled progress monitoring with smart contracts to automate and decentralize payments conditioned on progress assessments.
- Payments can transition autonomously from analyzed on-site observations when machine intelligence indicates incremental progress.
- Two commercial construction projects demonstrated the feasibility of using smart contracts for accurate payment processing, with accuracy dependent on both the smart contract and reality-capture system.
- Smart contract-based payments remain in early development, requiring standardized and formally verified contracts, fused data sources, and hybrid on- and off-chain settlement models.