AI in civil engineering: fundamentals, applications and developments

AI in civil engineering: fundamentals, applications and developments

Artificial intelligence has always been a far-reaching manifold technology with limitless potential across industries. AI in civil engineering took a central stage a long time ago with the advent of complex constructions such as skyscrapers. Today, we are witnessing the wide-scale adoption of artificial intelligence in civil engineering, with smart algorithms, Big data, and deep learning techniques redefining productivity performance.

With that said, let’s dwell on the current application of AI in civil engineering as well as go over the basics of amplifying construction with machine intelligence.

Artificial intelligence in civil engineering: fundamentals

According to McKinsey, the construction sector is one of the largest in the world economy. Its spending amounts to around $10 trillion that goes for construction-related goods and services every year. This number doesn’t seem so monstrous, though. A lion’s share of this amount is justified by the growing and much-needed tech innovations.

AI software development for construction is no different from other verticals. It is an umbrella term associated with machines developing human-like functions. The latter may include everything from problem-solving to pattern recognition.

However, machine learning in civil engineering is what steals the show since it lays the ground for most smart techniques in construction.

Machine learning in civil engineering

Construction projects pose a unique set of challenges due to their scale and the number of contractors involved. That is why civil engineering companies are turning to machine learning and data science consulting to help with the construction and design of roads, bridges, and other infrastructure projects. Historically, some machine learning algorithms were more popular than others in the field.

Evolutionary computation (EC)

Evolutionary modeling or computation is an AI category based on principles and concepts of evolutionary biology (i.e. Darwinian) and population genetics. Thanks to an iterative process, it offers an effective way to tackle complex optimization problems. This machine learning technique is widely applied in design engineering to automate design production. The typical evolutionary models used in construction include Genetic Algorithms, Artificial Immune Systems, and Genetic Programming.

Artificial neural networks (ANNs)

ANNs exhibit excellent performance in lots of areas, including construction. Artificial neural networks are modeled after the brain and can be trained to recognize patterns. This makes them useful for tasks such as decision making, pattern recognition, forecasting, data analysis. Civil engineering includes all those tasks. Thus, ANNs are widely present in studying building materials, defect detection, geotechnical engineering, and construction management.

Fuzzy systems

A fuzzy system is a way of reasoning that mimics the human way of thinking. It helps machines deal with inexact input and output in construction projects. These algorithms allow companies to model the cost, time, and risk of construction. Thus, fuzzy systems are also used for quality assessment of infrastructure projects at conceptual cost estimating stages.

Moreover, fuzzy logic is applicable for:

• Finding performance deviations during construction and forecasting relevant corrective measures
• Analyzing the impact of construction delays and adjusting the schedule estimating design cost overruns
• Identifying mark-ups for competitive bids
• Improving industrial fabrication and modularisation procedures, etc.

Expert system

Expert systems are also one of the most popular machine learning techniques for civil engineering problems. As such, the algorithm is based on the existing knowledge corpus of human professional experts to establish a knowledge system. This technique is widely employed in construction engineering, underground and geotechnical engineering as well as geological exploration. Thus, these algorithms can analyze the energy consumption of a certain building or group of buildings and offer suggestions for energy sources.

Overall, thanks to the growing adoption of machine learning techniques for civil engineering problems, AI in the construction market is projected to be worth over $2312 million by 2026. Some typical areas where AI is being used include highway design, traffic management, and construction planning.

Now let’s get over the real-life examples of artificial intelligence in buildings construction to further illustrate the significance of this technology.

Top engineering applications of artificial intelligence

The potential applications of AI in civil engineering are vast and diverse. From optimizing processes and improving product design, to automating tasks and reducing waste, AI has the potential to make a significant impact within the sector. Here are the most promising applications of AI within engineering.

Smart construction design

Constructing a building isn’t a one-day task that involves lots of pre-planning. Sometimes, it may take years to bring a particular vision to life. Therefore, the planning stage in construction has a lot to benefit from smart systems combined with Big data technologies.

Thus, AI-enabled tools and programs can now automate the calculation and environmental analysis. Instead of manually compiling weather data, material properties, and others, architects can automatically pull necessary data. Parametric design, for instance, has been one of the fields that have benefited the most from automated workflows.

Moreover, artificial intelligence has strengthened the core 3D construction system called BIM. BIM or Building Information Modeling allows architects to create data-laden models based on the comprehensive information layer.

The latter helps automatically create drawings and reports, perform project analysis, simulate the schedule of works, operation of facilities, and others. Due to unmatched analytical and future-telling abilities, smart algorithms can also assess resource-efficient solutions and create low-risk execution plans.

Moreover, machine intelligence can take the form of virtual and augmented reality. Both are now finding adoption in architecture to walk clients through lifelike experiences with ready designs. This way, clients have a better vision of the future project and can give actional feedback on further improvements without spending extra money.

Construction process orchestration

On-site construction management has always been agonizing for construction firms. According to McKinsey, mismanagement of building processes costs the construction industry $1.6 trillion a year. So, after using machine intelligence to assess structural damages in Mexico City after the earthquake, engineers have readily employed algorithms in other aspects of construction.

Autonomous construction monitoring with robots and UAVs is what makes real-time remote monitoring of construction sites possible. Unmanned aerial vehicles or drones fly over the construction sites and map the area with high-resolution cameras. After that, the system generates a 3D map and a report to be shared via the cloud with stakeholders. Also, drone maps’ geo tagging capabilities allow for the acquisition of relevant area measurements and the conversion of those measurements into an estimated stockpile volume for decision-making.

This way, both decision-makers, and workers stay safe and have a holistic vision of the ongoing progress. At the moment, autonomous inspection is not adopted en-masse due to the tech and resource limitations. However, the COVID-19 pandemic has accelerated the adoption by bringing automated systems on-site to check workers for symptoms and epidemiological factors.

Smart cities

Away from on-site usage to fundamental application, smart cities can be heralded as one of the most exciting and bold engineering applications of artificial intelligence. A smart city is a man-made interconnected system of information and communication technologies. This tech biota is inhabited by IoT and artificial intelligence to facilitate the management of internal urban processes. Ultimately, the main goal of this system is to make our lives more comfortable and safe.

To make our cities smarter, we need to use all the tools at our disposal. One of these tools is artificial intelligence (AI), which is being used more and more in smart city construction projects. For example, by using AI, planners can better understand how people move around cities and what kind of services and facilities they need. This helps to optimize the design of smart cities, making them more efficient and user-friendly.

Yet, the implementation of artificial intelligence in the smart urban landscape is more present in ready infrastructure. However, smart systems are still necessary at the construction stage to lay the ground for smart cities and design them in a technology-friendly way.

Construction 3D printing

Last but not least is a mind-boggling application of machine intelligence in 3D printing. In architecture, the construction of a building is a huge and costly undertaking. Not only do the architects have to design the building, but the engineers also need to calculate how it will stand up to wind loads, seismic forces, and other environmental stresses. Moreover, builders must find ways to make these structures not just habitable, but comfortable to live in, with features like air conditioning and insulation.

With 3D printing, a large part of the construction process is automated. As such, 3D house printing is the process of printing a three-dimensional object using a 3D printer. The object is printed by laying down successive layers of material until the entire object is created. 3D printers are now being used for house construction. The use of 3D printers for house construction has many advantages, including reduced waste and lower costs.

Thus, the AI Build company has already developed an AI-based 3D printing technology that can print large 3D objects at high speed and with great accuracy.

What awaits artificial intelligence in the construction field?

As we see from the current application areas, the future of AI in civil engineering is shrouded in potential but fraught with uncertainty. There are a number of ways that AI could be deployed within the civil engineering field, from the design and analysis of structures to the monitoring and maintenance of infrastructure.

However, due to the limited adoption, it is still hard to assess the whole significance of the technology for construction. Moreover, the use of artificial intelligence in civil engineering is still in its early days. Nonetheless, smart algorithms have great potential to improve the safety and efficiency of civil engineering projects. As AI technology continues to evolve, the possibilities for its application in civil engineering will continue to grow.

Author: Tatsiana Isakova

Source: InData Labs