AI in Structural Engineering: How Innovations Polish Approaches to Construction

Structural engineering establishes the reliability of civil structures to withstand challenging environmental conditions over time. Artificial intelligence can upgrade the building process, optimizing design, maintenance, and monitoring. It can replace time-consuming manual calculations with automated ones. Various simulations that cover different aspects of safety can save months of work, hundreds of thousands of dollars, and dozens of human lives.

Let’s take a glance at the most common challenges and prospects that AI integration can bring to the construction industry.

What AI-related Technologies Are Used in Structural Engineering?

The use of AI in structural engineering is multifaceted. Each step, from the initial design to construction calculations and preliminary testing, can be enhanced with the use of artificial intelligence. Requestum has prepared an insight into AI's role in engineers' work, and we will review how machine learning, deep learning, and pattern recognition are used in the industry for efficiency, productivity, and speed.

Machine learning

Engineers should understand how buildings can react to design safe and durable constructions. Machine learning (ML) analyzes structures' behavior under changing conditions and predicts possible outcomes. It helps to optimize the design and enhance construction safety.

By identifying patterns and analyzing sensor data, AI can check buildings' health and detect anomalies faster. It enables more accurate risk factor identification and automates inspection procedures. AI-powered monitoring allows better maintenance planning and timely repair work.

Deep learning

Deep learning (DL) is a part of machine learning that applies neural networks to model and understand complex data patterns. It can perform visual checks via images, detecting early signs of anomalies like corrosion and cracks. DL improves design generation, predictive modeling, and sensor data interpretation, enhancing precision and efficiency.

Deep learning can analyze seismic data and simulations to understand how earthquakes may affect construction and predict the structure's response accordingly. AI-powered calculations can forecast potential deformation of the building materials, the amount of damage done, and the location of actual weak spots. AI modeling can help companies optimize the material selection process to achieve better earthquake resistance and build to last.

Pattern recognition

Pattern recognition (PR) analyzes and detects structural performance and behavior patterns. A combination of statistical analysis, data visualization, and ML algorithms helps form accurate insights and enables informed decisions, giving engineers all the required data. It includes structural analysis, characterization, and failure identification. For example, PR helps to analyze the capacity and deformation of a specific part under various conditions. It also detects elements' fatigue and characterizes strength, stiffness, and ductility.

Application of Artificial Intelligence in Structural Engineering

As mentioned above, artificial intelligence is widely used for design optimization to simplify and organize processes. Automated monitoring allows for monitoring the state of the building 24/7 to quickly detect and inform about possible safety risks and take measures. So much human groundwork in structural engineering can be successfully performed by AI that it is worth getting into the details.

Structural design optimization

Machine learning algorithms can speed up cycling through possible options by generating structural designs automatically within the limits of set parameters. This AI-powered approach speeds up the processes, improving digital model creation and material selection. By examining generated alternatives, designers can optimize material use and enhance structural integrity. Smarter resource allocation paves the way to better cost-effectiveness.

Structural integrity monitoring

Artificial intelligence can help engineers develop proactive maintenance and repairs through continuous structural health monitoring. AI receives data from sensors and the environment and expands it with historical performance data to provide accurate predictions.

Engineers will not have to deal with endless data sorting if AI simply flags all critical factors. The faster reactions enable active risk management. With automated detection and identification of early signs of structural damage, AI can also estimate the approximate building lifetime. Companies informed about the need for urgent maintenance can plan repair work before the situation worsens.

Deep learning and computer vision allow engineers to automate visual data analysis for surface problem detection. Artificial intelligence processes all the images, detects, and classifies the type and location of issues to accurately represent the damage. The automated approach is far quicker than the human one, if we compare the speed and amount of data processed to conclude. It also significantly eases compliance with standard procedures and protocols. Deep learning and computer vision automate the analysis of visual data for surface problem detection for engineers.

Construction process automation

Artificial intelligence in construction goes far beyond virtual space, based on software and algorithms. Thanks to 3D printers, monitoring drones, rovers, bricklaying, painting, welding, and rebar-tying robots, builders experience their physical presence on the site. Self-operating heavy equipment and self-driving trucks can make human work easier, safer, and faster.

AI can also enhance quality control, detecting defects and damage at early stages and helping to fix them before they cause serious harm. During regular flyovers, drones can capture high-resolution images and videos to control progress and detect delays and anomalies.

Risk assessment and safety enhancement

Artificial intelligence analyzes large amounts of structural data to detect early signs of danger. A model trained on historical failure data can accurately predict the probability of potential structural failure caused by material issues, load patterns, or corrosion. Engineers can combine real-time monitoring and digital twin use to get constant updates and simulate potential collapse scenarios under various conditions.

Seismic resilience analysis helps with preparation for earthquakes. For instance, AI can simulate the effects on specific buildings to check their resistance and recommend changes. After earthquakes, AI can process records from drone footage and sensor data to check structural damage and mark emergency cases.

Advantages of AI Structural Engineering

Artificial intelligence has become a vital part of workflows in many industries because it boosts employee productivity by up to 40%. The surveys show that 97% of leaders believe that investments in technology lead to a positive return. For instance, AI brings 520 billion USD annually as an additional contribution to the construction industry. But what can it offer structural engineers specifically?

Increased accuracy

AI's precision allows engineers to achieve better design and structural analysis accuracy. It processes historical data, information about materials, environmental conditions, and other factors to optimize design choices. It enables super-accurate load calculations and improved stress and strain analysis.

Reduction in construction costs and project timelines

Optimized resource allocation helps organize the process better, select materials, and calculate the correct number of workers and equipment. By detecting potential design issues early, engineers can fix the problem without extra losses. AI's ability to predict delays and suggest adjustments helps meet deadlines.

Improved safety and risk assessment through predictive analysis

AI can analyze patterns of failures from previous projects and anticipate potential risks by identifying structural weaknesses. It can predict material degradation and fatigue, provide stress simulations, and assess resilience. An AI-powered approach helps implement proactive safety strategies and improve risk management.

Enhanced decision-making with AI-powered simulations

Artificial intelligence can simulate various scenarios and predict outcomes based on set conditions. For instance, engineers can see how a structure may respond to different loads or natural events and how specific materials may behave. It leads to more information-based decisions and better process arrangement.

Automation of repetitive tasks

Draft and document preparation, load calculations, and compliance checks may take time and effort. But AI can handle these tasks, freeing humans for other work. AI-powered material evaluation and quality control help achieve precise results faster, and engineers can deal with more complex problems.

Limitations and Challenges of AI in Structural Engineering

Utilizing any modern technology, you can experience life-changing benefits, but at the same time, you have to face some challenges and limitations, and AI engineering is no exception. As an AI development services provider, Requestum has formed a list of common difficulties to give you a complete picture of what to expect from AI implementation.

Difficulty in interpreting the decisions of AI for structural engineering

Artificial intelligence is a high-level tool that can provide conducive suggestions on how to improve engineering processes and results. Unfortunately, it is not always easy to interpret how exactly an AI-powered model comes to a certain conclusion. The so-called Black Box Problem demonstrates common struggles in explaining AI-made decisions that lead to further trust issues.

The development of explainable AI can help models become more interpretable. Decision transparency will increase engineers' trust and show AI-powered suggestions as more reliable.

The high initial cost of AI for structural engineering implementation

Artificial intelligence is not a solution that can be installed and left happily without additional control. It requires knowledgeable development, training, and maintenance. Initial implementation will require significant investments in software, hardware, and expertise.

Collaboration with the professional development team can significantly simplify the process. Experts know how to implement the model efficiently and how to provide it with high-quality preparation and training. They can adjust a pre-trained model or create a new one from scratch.

In-house engineering teams and other staff also need training to utilize AI tools efficiently and experience everything an upgrade can offer.

We recommend reviewing the situation as a long-term investment with high upfront costs and a high possibility of significant return.

Dependency on high-quality data for accurate predictions

AI models are trained on data, and information quality defines how well they will perform in the future. AI in structural engineering affects not only system efficiency but also the safety of the created constructions. Outdated, poor-quality, or biased data can cause inaccurate predictions and design weaknesses.

Training a high-quality AI model requires versatile and accurate datasets, including domain-specific information, possible environmental conditions, materials knowledge base, etc.

Ethical and regulatory concerns regarding AI-driven automation

When AI is used for structural design generation, the question arises: "Who bears responsibility if such a design fails?" There is also a common concern about whether engineers can trust AI to plan a life-critical infrastructure.

Artificial intelligence is a tool, and it needs guidance to operate correctly. Smart and responsible use is essential for construction safety. Clear regulatory frameworks, enhanced with ethical guidelines, will help AI to understand how far it can go in its decisions.

Need for continuous human supervision and expertise

Artificial intelligence is helpful, but remember it is not a replacement for an engineering team. Human supervision and expertise are an integral part of project success. AI can provide valuable reports, insights, and suggestions. It can assist with design generation and ensure quality. However, it lacks human creativity and intuition regarding complex problem-solving. AI can provide the required information, but it is the human engineer's task to interpret data for further use in designs and construction planning.

Future Trends and Perspectives

The National Council of Structural Engineer Associations has created a $100,000 fund for an AI initiative. It demonstrates how much the industry is engaged in AI development and the scale of trust put in a technology-filled future. According to the survey, artificial intelligence can enhance engineers' productivity by up to 45.55% over the next 20 years. Our experts also believe that AI development will augment engineers' capabilities and take building to another level.

IoT (Internet of Things) presence in the development process can enhance control, providing real-time data received directly from the sites. AR and VR technologies, in combination with robotics and digital twins, can offer better visualization of progress and stimulate faster decision-making.

Self-learning models for engineering will simplify upgrades, as developers will not have to create a model from scratch each time more data becomes available. These models will be able to evolve, collecting and learning from new and historical data and recording design specifications, test conditions, and results.

AI and BIM (building information modeling) will enable automated generation and optimization of structural models. With the ability to detect construction issues in early design stages, engineers can reduce the amount of rework and speed up design cycles. AI and BIM together will provide better visualization with the help of 3D model building, enhanced with information about materials, structures, costs, etc. It will enable shared space for architects, engineers, and builders for collective work and progress control.

Further development of quantum computing will also add more details to digital twins, raising the complexity of virtual systems. With a quantum approach, engineers can perform massive city-size simulations and analyze results in seconds.

Outcomes

Artificial intelligence can become a driving force for smarter, faster, and more technologically advanced workflows in the engineering industry. It helps achieve more accurate predictions and build safer constructions that withstand challenging environmental conditions. Future technological developments will help enhance structural engineering with complex digital models and real-time updates, AI-powered quality assurance, and high-level automation.

Now that you know how to use AI in structural engineering, and if you are ready to augment your team with AI capabilities, Requestum will be glad to develop and design a tailored solution for your construction company. Contact us, and let's discuss the future of engineering within your reach.