What is the vital difference between a digital twin and traditional simulation models? Rupesh Umtol, Global Head - Digital Twin and Allied Services, SoftTech Engineers, Pune, in conversation with R SRINIVASAN, responds to this and other aspects like real-time monitoring at sites, skill development, challenges faced during implementation, integration of data from AI, data security measures and emerging trends. Excerpts: What is the difference between a digital twin and traditional simulation models? Digital twins go beyond traditional simulation models by creating virtual replicas of physical assets, systems or processes in real time. While simulation models are typically static representations based on predetermined inputs and scenarios, digital twins continuously gather data from sensors, IoT devices and other sources to reflect the current state of the physical counterpart. This real-time synchronisation enables dynamic analysis, predictive insights and proactive decision-making, distinguishing digital twins from static simulation models. How do digital twins contribute to real-time monitoring at sites? Digital twins, in the context of Ayodhya and Dharavi, offer unparalleled benefits in real-time monitoring, planning and risk mitigation. By integrating data from sensors or data collectors embedded throughout the sites, stakeholders gain visibility into various aspects such as progress, resource utilisation, environmental conditions and safety compliance. This data-driven approach facilitates proactive decision-making, enhances design optimisation, identifies potential risks early and enables predictive maintenance strategies. Consequently, stakeholders experience improved project efficiency, cost savings, reduced downtime and enhanced safety measures, ensuring sustainable development in these complex environments. In Ayodhya, digital twins offer a nuanced approach to urban planning by integrating cultural heritage preservation with modern development initiatives. Beyond traditional models, digital twins enable stakeholders to simulate the impact of proposed changes on heritage sites, religious landmarks and archaeological treasures. By incorporating historical data, cultural documentation and community input, planners ensure sensitive preservation and sustainable growth. The interactive visualisation tools and scenario simulations foster stakeholder engagement, encouraging participatory decision-making and consensus-building. Moreover, digital twins support tourism management and cultural exchange by offering virtual tours and educational experiences, promoting Ayodhya’s rich heritage to a global audience. In Dharavi, digital twins serve as a catalyst for transformative urban development in Asia’s largest informal settlement. Real-time data on infrastructure, utilities and socioeconomic indicators inform inclusive planning, resource allocation and service optimisation. Residents become active participants in decision-making, as digital twins visualise proposed interventions, such as housing upgrades, better planning of public utilities and sanitation facilities. This participatory approach empowers community-led initiatives, fostering social cohesion and resilience. Disaster risk simulations aid in developing mitigation strategies, enhancing Dharavi’s ability to withstand natural hazards. By harnessing digital twin technology, Dharavi transitions towards sustainable development, inclusive growth and resilient communities, addressing urban challenges while improving the quality of life of residents. What challenges do organisations face during implementation? One significant hurdle, during implementation, is data integration from diverse sources, including legacy systems, IoT devices and third-party platforms. Ensuring interoperability, data consistency and quality across these disparate sources requires robust data management strategies and integration frameworks. Additionally, scalability, security and privacy concerns pose significant challenges, especially when dealing with sensitive information and regulatory compliance requirements. Moreover, cultural resistance, organisational silos and skill gaps may hinder its adoption and effective utilisation. How do digital twins contribute to skill development within the construction industry? Digital twins play a pivotal role in skill development within the construction industry by providing immersive learning environments and hands-on training opportunities. Through virtual simulations and interactive scenarios, construction professionals can gain practical experience in project planning, design validation, construction sequencing and maintenance procedures. Further, digital twins facilitate knowledge sharing, collaboration and best practice dissemination among project teams, fostering a culture of continuous learning and innovation within the industry. By empowering workers with digital tools and expertise, organisations can enhance workforce productivity, efficiency and adaptability to technological advancements. What measures are in place to ensure the security of data? Ensuring data security is paramount to safeguarding sensitive information and maintaining trust among stakeholders. Robust cybersecurity measures, including encryption, access controls, authentication mechanisms and audit trails, are essential to protect digital twin data from unauthorised access, tampering or data breaches. Compliance with data protection regulations such as GDPR and industry standards strengthens data security practices. Additionally, implementing robust data governance frameworks, risk management strategies and regular security audits helps identify and mitigate potential security threats, ensuring the integrity and confidentiality of digital twin data throughout its lifecycle. What are the challenges in integrating data from various sources into a cohesive digital twin? Integrating data from various sources into a cohesive digital twin for construction projects poses several challenges, including data interoperability, scalability and complexity. Different data formats, standards and semantics across disparate sources require harmonisation and normalisation to ensure consistency and interoperability within the digital twin environment. Advanced data integration techniques, such as semantic modelling, ontologies and data mapping, help bridge the gap between diverse data sources and facilitate seamless integration into the digital twin ecosystem. AI plays a crucial role in enhancing data integration processes by automating data discovery, transformation and reconciliation tasks. By leveraging AI-powered analytics and machine learning algorithms, organisations can derive actionable insights, perform predictive analytics and optimise decision-making processes within the digital twin environment. What types of sensors, technologies and data are used to create a comprehensive digital twin of a construction project? Creating a comprehensive digital twin of a construction project requires integrating various sensors, technologies and data sources to capture the physical, operational and environmental aspects of the built environment. Commonly used sensors include IoT devices, GPS trackers, LiDAR scanners, drones, cameras and environmental sensors, which capture real-time data on structural integrity, geospatial information, construction progress and environmental conditions. Additionally, building information modelling (BIM) data, CAD files, GIS data and project documentation provide valuable insights into design specifications, asset attributes and spatial relationships, enriching the digital twin model. Integration with enterprise systems, such as ERP, CRM and project management software, further enhances data completeness and interoperability, enabling stakeholders to make informed decisions and optimise project performance. What emerging trends do you foresee in the digital twin sector in the next five to 10 years? The sector is poised to witness several emerging trends that will revolutionise construction and infrastructure management. First, advancements in sensor technology, IoT devices and connectivity solutions will enable the proliferation of digital twins across diverse industries and applications, ranging from smart cities to industrial facilities. Second, the integration of AI, machine learning and predictive analytics will unlock new capabilities for real-time monitoring, predictive maintenance and optimisation of complex systems. Third, the adoption of Cloud computing, edge computing and distributed ledger technologies will enhance scalability, agility and resilience of digital twin platforms, enabling seamless integration with enterprise systems and IoT ecosystems. Fourth, the emergence of standards, interoperability frameworks and industry consortia will facilitate collaboration, data sharing, and best practice exchange within the digital twin ecosystem. Last, the democratisation of digital twin technology through open-source platforms, low-code development tools and industry-specific solutions will empower organisations of all sizes to leverage the benefits of digital twins for improved decision-making, innovation and sustainability. The digital twin would be the key tool for the built environment to set up and achieve the net-zero energy and carbon goal.