Construction Payment Automation Using Blockchain-Enabled Smart Contracts and Reality Capture Technologies

• URL: http://arxiv.org/abs/2010.15232v3
• Date: Tue, 2 Mar 2021 17:19:56 GMT
• Title: Construction Payment Automation Using Blockchain-Enabled Smart Contracts and Reality Capture Technologies
• Authors: Hesam Hamledari and Martin Fischer
• Abstract summary: 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. The method was successfully used for processing payments to 7 subcontractors in two commercial construction projects.
• Score: 0.8339831319589134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Smart Transport Infrastructure Maintenance: A Smart-Contract Blockchain Approach [0.0]
  Infrastructure maintenance is complex, especially for widely dispersed transport systems like roads and railroads. Traditionally, these requirements are managed on paper, with each contract step checked manually. Smart contracts, based on blockchain distributed ledger technology, offer a new approach.
  arXiv  Detail & Related papers  (2024-10-27T13:03:33Z)
• Decentralized AI: Permissionless LLM Inference on POKT Network [8.68822221491139]
  POKT Network's decentralized Remote Procedure Call infrastructure has surpassed 740 billion requests since launching on MainNet in 2020. This litepaper illustrates how the network's open-source and permissionless design aligns incentives among model researchers, hardware operators, API providers and users.
  arXiv  Detail & Related papers  (2024-05-30T19:50:07Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• Modelling Business Agreements in the Multimodal Transportation Domain through Ontological Smart Contracts [0.5735035463793007]
  The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract can be applied to a specific domain.
  arXiv  Detail & Related papers  (2022-09-05T09:58:42Z)
• Digital Enablers Of Construction Project Governance [0.0]
  This study examines the present technological advancements that can lead to fix this problem. A smart contract-based approach is ideal for managing construction progress payments. This article offers a way for automating construction payments by formalizing smart contracts execution on a decentralized block-chain-based system.
  arXiv  Detail & Related papers  (2022-05-12T07:44:16Z)
• A formal model for ledger management systems based on contracts and temporal logic [0.0]
  In second-generation blockchains such as the ledger is coupled with smart contracts. The current implementation of smart contracts as arbitrary programming constructs has made them susceptible to dangerous bugs. We propose here to recompose the split and recover the reliability of databases by formalizing a notion of contract modelled as a finite-state automaton.
  arXiv  Detail & Related papers  (2021-09-30T15:34:28Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)
• Regulation conform DLT-operable payment adapter based on trustless - justified trust combined generalized state channels [77.34726150561087]
  Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks. We give an overview of current solutions that differ in their fundamental values and technological possibilities. We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
  arXiv  Detail & Related papers  (2020-07-03T10:45:55Z)
• Local Differential Privacy based Federated Learning for Internet of Things [72.83684013377433]
  Internet of Vehicles (IoV) simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. In this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
  arXiv  Detail & Related papers  (2020-04-19T14:03:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.