In the field of civil engineering, AI is a driving force for innovation, efficiency and sustainability
The evolution of Artificial Intelligence (AI) has impacted the global industry but its influence on revolutionising the civil infrastructure, bringing a transformation in the way projects are designed, monitored and maintained is commendable. Employing its datasets and algorithms, AI offers insights that can optimise the entire infrastructure process while maintaining safety protocols.
Machine learning, deep learning, fuzzy logic, pattern recognition, decision trees, swarm optimisation and evolutionary computations are some of the different branches of AI that can be used in civil engineering.
Key Applications of AI in Civil Engineering:
Damage detection: To detect damages using sensory or image data, identifying its location and extent
Productivity enhancement: To enhance productivity by reducing idle time
Concrete density prediction: To predict density and optimum moisture content in concrete
Site monitoring and safety: To use image recognition for site monitoring and ensure safe working conditions
Building Information Modelling (BIM): To ascertain efficient planning, designing and managing of infrastructure using BIM
Foundation and slope stability analysis: To analyse settlement of foundation and slope stability
Error reduction: To reduce errors in the project by automatic analysis of data
Site layout and risk prediction: To develop site layouts and predicting risks as part of project management
Design decision-making: To make decisions in the design field
Waste management: To handle waste management and smart materials
Cost monitoring and optimisation: To monitor and optimise costs in the work system
As we focus on AI’s multifaceted role in civil engineering, let us take a closer look at its transformative use cases and the solid impact it has on civil infrastructure:
Building Better Structures using Generative Designs
Building Information Modeling (BIM), a 3D model-based paradigm offers invaluable insights into the intricate tapestry of engineering, architecture, and construction within a project. Integrating machine learning into generative design represents a significant leap forward, enabling the detection and resolution of conflicts during the planning and design stages. In civil infrastructure, a remarkable shift has progressed from 3D modelling to the cutting-edge integration of 7D. 4D enabled stakeholders to visualise construction sequences, 5D assisted with cost integration and 6D was helpful for sustainability considerations. The use of 7D incorporates AI, machine learning and data analytics further enabling predictive insights and real-time monitoring throughout the entire infrastructure lifecycle. This transition to 7D empowers the industry with unprecedented tools to manage and sustain infrastructure in a complex and dynamic environment.
Making Job Sites more Productive
To ease repetitive jobs, there are companies that are providing self-driving construction machinery leading to efficient results in comparison to their human counterparts. These can be for tasks such as pouring concrete, masonry, welding and demolition. Excavation and other preparation work can be performed by autonomous or semi-autonomous material handling equipment with the assistance of a human programmer to the required specification. This enables the human staff to focus on the actual construction work while reducing the overall time of the entire process. The use of AI-driven metrics, the Internet of Things (IOT) enables real-time location tracking, predictive maintenance, fuel and battery monitoring. The integration of IoT devices can help predict equipment breakdowns and address other concerns further resulting in saving time and cost respectively.
Maintaining Construction Safety
The fatality rate in the construction industry is higher compared to other industries. Some of the main reasons include falls due to electrocution, material handling incidents and crashes. The use of AI solutions will help analyse CCTV footage and correlation of these incidents with health and safety measures and the adherence to them by affected workmen. Such AI solutions aid risk rating in projects while safety measures are devised to include safety briefings to control/address the risk.
Founder & Managing Director
ABM Knowledgeware Ltd.
Addressing Labour Shortages
The lack of labour and a need to accelerate the industry’s low productivity levels are reasons enough to compel the construction industry to invest in AI and information science. According to a McKinsey report, construction corporations may boost productivity by 50% using information and predictive analysis. The use of AI and machine learning will better organise machinery and labour across multiple jobs.
Artificial Intelligence has transformed the traditional way in which it used to be undertaken. Now with its influence, most work tends to be smarter and resourceful. Similarly, in the field of civil engineering, AI is a driving force for innovation, efficiency and sustainability. With AI’s ability to analyse vast datasets, optimise designs and predict outcomes, it is poised to influence the way in which we plan, design and forecast an infrastructure project.
