The use of digital twins & AI in improving sustainable outcomes

Engineering Sustainability upcoming Themed Issue

Submit your abstract by 30 April 2025

Introduction

A digital twin is a virtual representation of an object or system designed to reflect a real-world object accurately. They can range from a simple, stand-alone virtual  interpretation of a product component to an intricate, dynamic representation of the lifecycle activities and assets within a building or even an entire city. They can be updated from real-time data and use simulations, machine learning and reasoning to help make decisions. 

Digital twins are designed to enable a two-way flow of information. This occurs when object sensors provide relevant data to the system processor and then again when insights created by the processor are shared back with the original source object. This continuous data exchange, combined with the significant analytical capability of modern computers, allows digital twins to monitor issues from a far wider vantage point than standard simulations, with greater potential to improve outcomes.
Artificial Intelligence (AI) can significantly enhance the capabilities of digital twins. By integrating AI with digital twins, vast amounts of data can be processed more efficiently and accurately. AI techniques such as machine learning, predictive analytics and optimisation enable digital twins to simulate real-world scenarios, predict outcomes and provide actionable insights with often higher levels of precision compared to humans.

Arguably, the increasing affordability of sensors and the collection of vast quantities of data feeding the digital twin combined with the power of AI allows for a level and speed of analysis and problem rectification and insights that was inconceivable just a few years ago. 
Proponents of digital twins and AI point out the benefits of more informed and creative design choices, reducing and eliminating errors in real world assets through more interactive design and the use of monitoring facilitated by the Internet of Things  (IoT) to be able to proactively optimise asset operation and undertake proactive maintenance activities that can both improve reliability and reduce energy consumption and prolong asset life.

For example, Kinebar et al, 2023 state that digital twins have been increasingly used in construction in recent years pulling in data from Building Information Modelling (BIM) software, and  IoT devices. In construction, digital twins can be used to simulate the construction process, optimise design, detect potential problems, and monitor the building's performance throughout its lifecycle. Digital twins can test different construction methods and predict how they will impact the building's energy consumption, structural integrity, and overall sustainability (Preuveneers et al., 2018).  This all points to the increasing value of digital twins in supporting the long-term sustainability of our built assets.

In Singapore, digital twin technology helps city planners understand and improve the efficiency of energy consumption as well as many applications that can improve life for its citizens. 

This call for abstracts seeks contributions from authors that:

• Can describe real-life examples of where the synergies between digital twins and AI exist in the design, construction and operation of built assets and how this is contributing to their sustainability.
• Are undertaking digital twin and AI R&D that can help to improve the sustainability of our built assets.
• Have encountered problems or limitations with digital twins and/or AI in the built environment and how these have been resolved.
• Can illustrate how digital twins and/or AI can start out on a small scale and grow over time, in line with data quality and understanding. 
 

Abstracts for papers that cover any aspect of civil engineering across all types of infrastructure are welcome, as are papers that draw on the lessons learned from other industries and sectors and apply them to built assets. Authors should clearly draw out the sustainability benefits of digital twins and AI and, where possible, are encouraged to set out any intellectual and commercial investment needed to bring such technologies into use.

Submission information

Submit your abstract by 30 April 2025

Author guidelines must be strictly followed

If your abstract is successful, you will be invited to submit your full paper here: https://ice-review.rivervalley.io/journal/jensu. Once you have registered, navigate to the journal that you wish to submit to. Choose article type "Themed Issue" and then the specific name from the drop-down menu on screen.