Artificial Intelligence in Construction: Part III

Joseph A. Cleves, Jr. | Taft Stettinius & Hollister

As we noted in our first article on artificial intelligence in construction, artificial intelligence (AI) is a broad term that generally refers to technology that uses algorithms to process data and simulate human intelligence. In our first two articles, we discussed machine learning and then image recognition and sensors-on-site. In this article, we discuss two more AI-related topics: (1) building information modeling; and (2) smart contracts.

Building Information Modeling

Building information models (BIMs) are three-dimensional, digital, construction blueprints. BIMs allow numerous project participants to view and modify the same model. They are generally highly detailed, allowing users to access information on each building.

BIMs offer several benefits over prior practice. They provide participants with the capacity to visualize and comprehend a design much more easily. They also allow for better communication between participants by constantly updating the design as changes are made. BIMs can also lead to improved design quality, detail, and precision because of their digital form. Finally, because BIMs are constantly updated, they allow owners to monitor a project closely for deviation from the original plan. These benefits may also reduce the risk of liability in some cases.

However, BIMs also create several new risks of liability, two of which we note here. First, the roles and responsibilities of participants can become irreversibly intertwined in a BIM. In other words, it may become impossible to ascribe responsibility, and therefore fault, to the correct actor when numerous actors are given broad decision-making authority. Given all the hands touching the design documents, it is very important, in particular, to define the design responsibilities carefully. 

Second, BIMs create intellectual property right concerns. The traditional rule is that the party that creates the model owns it. But since BIMs are often compiled from information contributed by numerous sources and parties, the situation becomes more complicated. The solution to this issue is to address it in the contract. If parties fail to do so, they will be left to follow a convoluted web of information to locate the “true owner” of the model.

“Smart Contracts” and Blockchain Technology

“Smart contracts” is a phrase used to describe computer code that automatically executes all or parts of an agreement automatically and is stored on a blockchain-based platform. A blockchain refers to a decentralized, online ledger, which holds a time-stamped series of immutable records of data managed by a cluster of computers. 

There are two types of blockchains: public and private. Public blockchains are decentralized and allow anyone to join the network and participate in the blockchain. Once transactions are validated, the data is secured and cannot be modified or altered. Private blockchains, on the other hand, limit who can access and participate in the network and are typically controlled by one or more entities. Accordingly, only parties participating in a transaction can access private blockchains. Similar to public blockchains, once transactions are validated in a private blockchain, the data cannot be modified or altered. Large networks of computers are used to verify blockchain transactions and store blockchain data. The use of blockchain technology can eliminate transaction fees and the need for third-party verification typically required in certain transactions. 

Smart contracts, like traditional contracts, define the rules and liabilities of the parties. The difference, however, is that smart contracts automatically enforce their obligations and liabilities. Once operational, smart contracts generally require no human intervention to execute and enforce their terms. Smart contracts are currently suited for simple transactions. An example is automatically transferring funds from one party to another when specific criteria are met and imposing damages if certain conditions are not met. Hybrid-contracts, however, consist of a traditional written contract alongside a smart contract to cover an automated function, such as payment.

Implementing fully autonomous smart contracts in the construction industry presents many issues. One is the need for unique code to accompany every smart contract. While common transactions can recycle the code from other smart contracts, every construction contract contains significant elements that are unique that would require unique code. Another issue arises from the need for funds to be pre-loaded into a digital wallet so that the smart contract can automatically execute its payment obligations. Construction projects can be expensive. Requiring a party to advance funds into a virtual wallet until completion is likely not a viable option. Additionally, fully autonomous smart contracts would likely require some reliance on outside information that cannot be anticipated in the contract code. Data from image recognition and sensors-on-site, for example, will need to be continuously updated in a fully automated smart contract. Outside data reliance for smart contracts poses what many refer to as the “oracle” problem. Outside data can only be provided to smart contracts through manual input. Thus, it would require a party to hire someone who specializes in providing data for smart contract codes. Utilizing an oracle will create additional fees and less autonomy as the smart contract would fail if the oracle fails to relay the outside information. Further, parties using an oracle must trust that the oracle will adequately perform its duties as mistakes could render a smart contract useless.

Smart contracts also pose some important legal issues. Because data shared on blockchain technology cannot be altered or modified, it is virtually impossible to change the terms of the contract. Where modification is necessary, new smart contracts must be created. Additionally, courts will likely struggle to adjudicate smart contracts and blockchain technology due to a lack of familiarity with the new technology. One way some parties have nonetheless sought to take advantage of the benefits of smart contracts, while addressing their limitations, is to use hybrid-contracts that contain elements of both traditional and smart contracts. This allows some automation and provides security for parties by having a written contract that can be easily read and interpreted by a court. For this reason, hybrid-contracts appear to hold the most promise for industry-wide application. 

Be a Good Neighbor: Protect Against Claims by an Adjacent Landowner During Construction

Joshua Levy and Madeleine Bailey | Construction Executive

There’s nothing like working in an office while pilings are being pounded into the ground next door, leading to crashing sounds of pile driving and the attendant afternoon headaches. Fortunately, that’s often the extent of a neighboring project’s real inconvenience. In other cases, however, construction in close quarters can mark the beginning of costly and emotional disputes, which can escalate to costly legal battles during and after construction.


Construction claims are often based on the concept of “nuisance,” or on structural damage to adjacent property. Nuisance claims are typically based on noise and dust from construction sites, while structural damage claims are based on direct physical damage caused by neighboring demolition, vibrations, excavation and dewatering. These types of claims can result in monetary damages for neighbor plaintiffs, loss of permits for contractors and reputational damage to the developer.

In one recent case in New York City, the developer faces up to $10 million in damages in a lawsuit with a neighboring property owner. The developer was conducting excavation, dewatering and installation of steel sheet piles, which the plaintiff alleges caused its five-story building to settle and shift, rendering doors inoperable and causing extensive cracking and separation of floors and ceilings from walls and supports. The plaintiff filed its complaint on Jan. 24, 2019, and the lawsuit is ongoing, exemplifying that construction claims such as these can be time consuming and costly (Complaint, 642 East 14th St. v. 644 E. 14th Realty [N.Y. Sup. Ct. January 24, 2019]).

Non-monetary costs associated with adjacent property damage claims can also be steep. In one infamous Philadelphia case, a construction crew destroyed a shared foundation wall while working underground, causing the ceiling of the neighboring rowhouse to cave in, and the stairs to separate from the wall. The city ordered demolition of the neighboring house, revoked the contractor’s permits and ordered a district attorney investigation of the incident.

Nuisance claims can be similarly costly. In a Texas nuisance case, plaintiff homeowners sued a developer constructing a project near their homes, alleging that vibrations, lights and noise caused “loss of use and enjoyment” of their properties. The court upheld an award of more than $200,000 to the neighbors even though the developer held proper city permits. The court specifically relied on the facts that the contractor worked “around the clock” for approximately four months, including weekends and holidays, using bright lights placed directly behind the plaintiffs’ homes to illuminate the worksite at night. Some of this around-the-clock work included excavation work performed within 20 feet of one of the plaintiff’s homes. The court held that these actions were “abnormal and out of place.” ( C.C. Carlton Indus. v. Blanchard, 311 S.W.3d 654 [Tex. App.—Austin, 2010, no pet.])


High-risk projects in urban or high-density areas also put developers at risk of being sued by neighbors falsely claiming that preexisting damage was caused by the developer’s construction. Preconstruction surveys can save developers from opportunistic neighbors by debunking claims that they caused such damages.

One New York court reversed a previous injunction which prevented a developer from construction based on evidence shown in a preconstruction survey. In this case, the plaintiff alleged property damage resulting from excavation work. The court specifically relied on an independent engineering report showing that the damages alleged by the plaintiff were actually preexisting as shown by the preconstruction survey. (Feldman v. 3588 Nostrand Ave. LLC, 2020 NY Slip Op 31274 [U], ¶ 18 [Sup. Ct.])

Minimizing potential nuisance claims is a bit simpler. Developers can mitigate this risk by maintaining appropriate work hours and good worksite housekeeping practices. Also, developers should conduct a thorough review of the jurisdiction’s noise and vibration ordinances to ensure compliance. Proactive developers may consider visiting neighbors in advance to review the days and times when more obtrusive activities will take place.

Finally, while nuisance and structural damage claims can result in costly damage awards, the potential costs to goodwill between neighboring property owners should not be overlooked. Recognizing the disruption a project will cause and implementing disturbance mitigation measures can help owners and contractors avoid neighbor disputes.

This is the first article in a three-part series. Parts two and three will review preventative measures that can mitigate the relational fallout from construction incidents, and minimize the chances a construction project is tied up in costly and time consuming litigation.

Artificial Intelligence in Construction: Part I

Joseph Cleves, Jr. | Taft Stettinius & Hollister


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

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.

Guidance for Construction Leaders: How Is the Americans With Disabilities Act Applied During the Pandemic?

Molly Gwin | Construction Executive

With the spread of the COVID-19 pandemic, numerous cities and states have mandated infection control practices, including social distancing, mask requirements and sanitization of work areas and tools. As a result, many construction leaders now have questions as to how government guidance related to COVID-19 interacts with the Americans with Disabilities Act (ADA). For example, can a project manager enforce a mask mandate when a construction worker presents a doctor’s excuse noting breathing difficulties? Or, what if the employer is aware that an individual presents a higher risk for severe illness because of an underlying health condition, but that employee does not request an accommodation? 

Thankfully, the United States Equal Employment Opportunity Commission (EEOC) recently published guidance relating to these requests that construction leaders can reference. While our goal is to summarize that guidance and provide practical advice for the construction sector, this article does not substitute for situation specific legal counsel.


Potentially. Since the request to not wear a mask is considered an accommodation under the ADA, the employer can still require a doctor’s note when considering the accommodation. 

The employer and employee should consider the following factors in determining if the accommodation is necessary: Does the stated disability limit the wearing of a mask? Does the requested accommodation address the limitation? Could another form of accommodation address the issue instead? Does the proposed accommodation enable the employee to continue performing the essential functions of his or her job?

Upon consideration, the employer has the right to decline the requested accommodation if such request would pose an undue hardship on the operation of the employer’s business under the ADA. That said, in administering workplace restrictions imposed by the COVID-19 pandemic, employers should also take care not to engage in disparate treatment based upon protective classes for members of the workforce.


If the employer is concerned about the employee’s health, the ADA does not allow the employer to take any adverse action simply because the employer is aware that the employee is at a higher risk for severe illness. Similarly, if the employee does not request an accommodation, the ADA does not require that the employer act. 

Employers should consider that a request for an accommodation may be made informally unless the employer can show that such an activity is a direct threat to the health of the employee. In this case, a direct threat defense requires the employer to show that the employee returning to work poses a “significant risk of substantial harm” to the employee or their colleagues.

Please note that, unless there is no way to provide a reasonable accommodation for the employee, an employer cannot exclude the employee from the workplace based on a direct threat defense.


The EEOC’s guidance indicates, “[D]uring the pandemic, ADA covered employers may ask the employee if he or she is experiencing symptoms of the virus.” Symptoms regarding COVID-19 are published on the CDC’s website as well as by state and local public health agencies. If employees are experiencing COVID-19 symptoms, employers may wish to direct employees to self-quarantine. 

Please keep in mind all information relating to the employee’s health condition must be maintained as a confidential medical record.

Construction-related business owners and project managers should be mindful that guidance from public health officials is ever evolving during the pandemic and they should continue to monitor guidance from public health offices in order to maintain workplace safety on construction sites. For ongoing updates, please visit the

Guidance on Using Drones for Real Estate and Construction in Dense Cities: Getting Close – But Not Too Close (Part I)

Virginia Trunkes | Construction Law Zone

The commercial use of drones, or small unmanned aerial systems (sUAS), for urban real estate and construction may finally be gaining traction. This month, the New York City Council passed a bill requiring the Department of Buildings (DOB) to study the feasibility of using sUAS to inspect building facades.

Compliance with the city’s Facade Inspection and Safety Program, which requires owners of buildings higher than six stories to conduct facade inspections and make the needed repairs every five years, can be expensive and cumbersome. As opposed to manual examinations, sUAS can get close to building facades and “hard-to-reach” locations faster and more adeptly, and deliver high-quality pictures to the remote pilot. The pilot in turn can simultaneously transmit the images to back-office engineers who can convert the aerial images into 3-D models, and therefore gauge the solidity of structures.

Yet, a 1948 Local Law prohibits any and all sUAS usage within New York City’s borders. Administrative Code § 10-126(c), entitled, “Take offs and landings,” provides: “It shall be unlawful for any person avigating an aircraft to take off or land, except in an emergency, at any place within the limits of the city other than places of landing designated by the department of transportation or the port of New York authority.”

As the 2020 bill’s sponsoring Council Member, Paul Vallone, commented: “An outdated local law, drafted decades before the advent of … ‘drones,’ is leaving New York City on the ground while other cities are already using rapidly advancing technologies to support business and improve safety.” Effective September 16, 2020, the bill takes effect immediately and the study must be completed no later than Oct. 31, 2021.

The DOB’s mandated study comes upon the expiration of the UAS Integration Pilot Program (IPP). Established by an October 25, 2017 Presidential Memorandum, the IPP is a consortium of state, local, and tribal governments, along with private sector stakeholders and experts, charged with “advanc[ing] the UAS industry”  by using “the information and experience yielded by the [IPP] to inform the development of regulations, initiatives, and plans to enable safer and more complex UAS operations … .” In other words, the IPP is to determine how to best integrate sUAS into the national airspace.  The IPP’s challenge is to evaluate and balance sUAS operations with local and national interests, including ever-increasing privacy concerns. The program is set to expire by its own terms on October 25, and recently, the FAA declined to extend it. That means, according to the Presidential Memorandum, that within 90 days thereafter, the FAA “shall submit a final report to the President setting forth the Secretary’s findings and conclusions concerning the Program.”

While the United States waits, the European Union Aviation Safety Agency (EASA) has already assessed how to integrate sUAS into aircraft space. Earlier this year, EASA published a proposed regulatory framework along with its explanatory “Opinion” for “creat[ing] and harmoni[zing] the necessary conditions for manned and unmanned aircraft to operate safely in the U-space airspace, to prevent collisions between aircraft and to mitigate the air and ground risks.” “U-space” is Europe’s unmanned air traffic management framework. The initial scope of the proposed regulations involves low level airspace, densely-populated urban airspace and locations close to airports.

EASA is the first administrative agency in the world to formally examine what is needed to ensure safety and privacy, and limit an environmental impact, in an urban environment while establishing a competitive market in “U-space services”. EASA recommends using a common information service that will enable the exchange of essential information among the U-space service providers, the UAS operators, the air navigation service providers and all other participants in the U-space airspace. According to EASA, “[o]nly a clear EU regulatory framework can establish a competitive European U-space services market to attract the necessary business investments in both the UAS and U-space services markets.” EASA anticipates that the European Commission will use its draft regulation in order to prepare a European Union regulation following its consultation with the necessary stakeholders.

Using one, large information service that will facilitate the transfer of aircraft information inherently raises privacy questions. So too does future use of sUAS traversing around city buildings. It remains to be seen whether the IPP and European Commission will evaluate privacy concerns similarly – and which if any of the two constituencies will delay enactment of legislation because of those concerns.