Bienvenue sur la Grande Bibliothèque du Droit ! Ceci est une bibliothèque contributive. Vous pouvez nous proposer des articles.
La Grande Bibliothèque du Droit est une bibliothèque juridique en ligne, en accès libre et gratuit, créée par le Barreau de Paris.Les lecteurs et contributeurs ne doivent pas oublier de consulter les Avertissements juridiques.
Welcome to the Grand Law Library ! This is a participatory e-library. You can send us your publications
Artificial Intelligence in Construction: Part I (us)Version imprimable
Auteur: Joseph Cleves, Jr., Avocat et Associé 
Date: 2 octobre 2020
Artificial Intelligence (AI) is a broad term that generally refers to technology that uses algorithms to process data and simulate human intelligence. Examples of AI technology include machine learning, image recognition and sensors-on-site, building information modeling (BIM), and “smart contracts” stored on a blockchain-based platform. This technology can be used in the construction industry by way of design, operations and asset management, and construction itself. Construction leaders interested in staying ahead of the curve should consider its advantages, and the legal implications.
This article will discuss our first AI-related topic: Machine learning. In three subsequent articles we will discuss (1) image recognition and sensors-on-site; (2) building information modeling; and (3) smart contracts.
Machine learning is a subset of AI, but it is the basis for the vast majority of AI technology. Machine learning at its core is a simple process: using an algorithm and statistics to “learn” from huge amounts of data. The data doesn’t have to be just numbers; almost anything that can be digitally stored or recorded can be used by a machine learning algorithm. This type of technology can be used to recognize patterns, extract specific data, make data-driven predictions in real time, and optimize many processes.
Machine learning’s ability to process and detect patterns in large amounts of data makes the technology ideal for data-intensive tasks like scheduling and project planning. To aid in project planning, machine learning technology can include the process of “reinforcement learning.” That is when an algorithm applies automatic trial and error. This is different than the usual process of humans collecting, labeling, and categorizing the underlying data that machine learning relies on. The autonomous process of reinforcement learning allows the technology to offer optimized suggestions efficiently and continuously based on previous, similar projects. It also allows the technology to help assess risk in a project, constructability of a project, and various materials and technical solutions for a project.
Firms can use machine learning to identify risks, such as when certain assets will need maintenance, by using data on various machines and equipment. The machine learning technology then analyzes the data to predict when preventive maintenance will be needed. This can increase efficiency by avoiding the need to take assets out of operation due to a breakdown.
These examples of risk management and project and design optimization just scratch the surface of how machine learning can be applied. This technology can optimize virtually any process that generates data, such as bidding, pricing of fixed-price contracts, recruiting and talent retention, and inventory management. To begin implementing machine learning, firms should identify processes where optimization from this technology would maximize return on investment.
For companies interested in using machine learning, it will be important to address the issue of risk allocation in the contract documents because the state of the applicable law is not clear. The parties should map out precisely who will own the risk associated with the technology and what degree of liability a party is taking on. This issue is especially important depending on who owns the technology – the construction firm, or a third party. If a construction firm owns the majority of the risk associated with the technology, then the adoption of machine learning technology in construction may decline.
Lastly, the parties will need to determine who will own the data that the technology records and uses and whether the data needs to be protected. Parties will need to determine how that data can be used by the company supplying the technology or other third parties, if at all. That issue is particularly relevant if the technology is provided by third parties who want to use the construction firm’s data to refine their technology. And, if the data needs to be protected, the parties will need to negotiate contract terms that dictate the protection protocols.
This article has just scratched the surface. Look for the next installment on image recognition and sensors-on-site next month.