Understanding Digital Twins

For years, we talked about Digital Twins as a visualisation tool. A smarter, live version of the BIM model. Something impressive to show clients in a project review. That conversation has shifted dramatically in 2026. AI-driven Digital Twins are moving beyond dashboards toward self-learning systems that continuously refine predictions as more data is collected. We are not talking about a model that reflects reality. We are talking about one that anticipates it. What does that actually mean on the ground? It means maintenance schedules driven by live sensor data, not assumption. It means risk thresholds triggering automated recommendations before a problem becomes an incident. It means the gap between design intent and operational reality finally starting to close. Interoperability is becoming a priority, with increasing focus on open standards and integration across BIM, GIS, IoT, and asset management systems. The siloed platform era is ending. The connected data ecosystem era is beginning. Digital models are no longer ready to be built. They are being developed as long-term operational resources on which maintenance plans, financial plans, and sustainability performance are based. This is the lifecycle shift our industry has been talking about for a decade. It is now happening in practice. The question is not whether your organisation needs a Digital Twin strategy. The question is whether your data is structured well enough to feed one. Is your information ready for what comes next? #DigitalTwin #BIM #InformationManagement #AssetManagement #DigitalConstruction #AI

What if your AI could predict years of real-world performance after just days of testing? IBM Research has developed a new generation of AI-powered digital twins by applying foundation model techniques, the same deep learning architectures behind today's large language models (LLMs) to physical systems like batteries. Traditional digital twins (virtual simulations of real-world systems) have struggled because it’s incredibly hard to model the full complexity of physical systems accurately. IBM's innovation changes this: instead of manually building physics models, they train AI models on real-world sensor data to predict system behavior. These digital twins are data-driven, self-improving and can simulate complex behaviors with high precision. The first major application is in electric vehicle (EV) batteries, where IBM partnered with German company Sphere Energy. Developing and validating a new EV battery can take years because manufacturers have to physically test how batteries perform and degrade over time. Using IBM’s AI-powered digital twins, manufacturers can now simulate years of battery aging and usage after only a small amount of real-world testing. Sphere's models predict battery degradation within 1% accuracy, which wasn’t possible before with traditional simulations. Technically, IBM’s digital twins use a transformer-based encoder-decoder architecture (like a language model) but are trained on numerical sensor data (voltage, current, capacity, etc.) instead of text. Once trained, the model can generalize across different batteries or vehicles, needing only minimal fine-tuning — which saves huge amounts of time and money. The impact is huge: up to 50% faster development cycles, millions of dollars saved, and faster adoption of new battery technologies. Beyond EVs, this technology could also transform industries like energy, aerospace, manufacturing, and logistics by providing faster, real-time, AI-driven system modeling and predictive maintenance. Learn more: https://buff.ly/JAzctHa #IBM #IBMiX #AI#genAI

One of the most transformative digital tools applied in #cement grinding is the #digitaltwin — a real-time virtual replica of physical equipment and processes. By integrating #sensordata and process models, digital twins enable engineers to simulate process variations and run “what-if” scenarios without disrupting actual production. These simulations support decisions on variables such as #grindingmedia charge, mill speed, and classifier settings, allowing optimisation of energy use and product fineness. Digital twins have been used to optimize #kilns and grinding circuits in plants worldwide, reducing unplanned downtime and allowing predictive maintenance to extend the life of expensive grinding assets. While #digital technologies improve control and prediction, materials science innovations in grinding media and grinding aids have become equally crucial for achieving performance gains. Traditionally composed of high-chrome cast iron or forged steel, grinding media account for nearly a quarter of global grinding media consumption by application, with efficiency improvements translating directly to lower energy intensity. Recent advancements include #ceramic and #hybridmedia that combine hardness and toughness to reduce wear and energy losses. For example, manufacturers such as Sanxin New Materials in China and Tosoh Corporation in Japan have developed sub-nano and zirconia media with exceptional wear resistance. Complementing #grindingmedia are grinding aids — chemical additives that improve mill throughput and reduce energy consumption by altering the surface properties of particles, trapping air, and preventing re-agglomeration. Technology leaders like SIKA AG and GCP Applied Technologies have invested in tailored grinding aids compatible with AI-driven dosing platforms that automatically adjust additive concentrations based on real-time mill conditions. Trials in South America reported throughput improvements nearing 19% when integrating such digital assistive dosing with process control systems. The integration of grinding media data and digital dosing of grinding aids moves the mill closer to a self-optimizing system, where AI not only predicts media wear or energy losses but prescribes optimal interventions through automated dosing and operational adjustments. Heidelberg Materials has deployed digital twin technologies across global plants, achieving up to 15% increases in production efficiency and 20% reductions in energy consumption by leveraging real-time analytics and predictive algorithms. Holcim’s Siggenthal plant in Switzerland piloted AI controllers that autonomously adjusted kiln operations, boosting throughput while reducing specific energy consumption and emissions. Cemex, through its AI and #predictivemaintenance initiatives, improved kiln availability and reduced maintenance costs by predicting failures before they occurred. Read my full article in the February’26 issue of Indian Cement Review.

"The Role of Digital Twin Technology in Bridge Engineering." With the rapid advancement of digital technologies, the construction and maintenance of bridges are evolving beyond traditional engineering methods. One of the most transformative innovations in recent years is Digital Twin Technology, which is reshaping how we design, monitor, and maintain bridges by integrating real-time data, predictive analytics, and AI-driven insights. What is a Digital Twin? A digital twin is a virtual replica of a physical bridge that continuously receives real-time data from IoT sensors embedded in the structure. These sensors monitor structural conditions, load distribution, environmental impacts, and material fatigue, creating a dynamic and interactive model that mirrors the actual performance of the bridge. This virtual model allows engineers to simulate different scenarios, detect anomalies early, and optimize maintenance strategies before actual failures occur. How Digital Twins Are Revolutionizing Bridge Engineering 1. Real-Time Structural Health Monitoring (SHM) IoT sensors collect continuous data on factors such as temperature, stress, vibration, and corrosion. AI-powered analytics process this data to identify patterns of deterioration and potential structural weaknesses. Engineers can access real-time insights from remote locations, reducing the need for frequent on-site inspections. 2. Predictive Maintenance & Cost Efficiency Traditional maintenance relies on scheduled inspections, often leading to unnecessary costs or delayed repairs. With digital twins, predictive analytics help forecast which parts of a bridge will require maintenance and when, optimizing repair schedules. This proactive approach extends the lifespan of the bridge and reduces long-term maintenance expenses. 3. Simulation & Risk Assessment Engineers can simulate extreme weather conditions, earthquakes, and heavy traffic loads to assess a bridge’s resilience. This allows for better disaster preparedness and risk mitigation, ensuring public safety. In construction projects, digital twins can be used to test different design alternatives before actual implementation. 4. Sustainability & Smart City Integration By optimizing material usage and maintenance, digital twins help reduce environmental impact. They also enable better traffic flow analysis, contributing to the development of smarter and more efficient transportation networks. Integrated with Building Information Modeling (BIM) and Machine Learning, digital twins are a key component of smart infrastructure development. Video source: https://lnkd.in/dkwrxGDE #DigitalTwin #BridgeEngineering #SmartInfrastructure #CivilEngineering #StructuralHealthMonitoring #Innovation #IoT #BIM #AIinConstruction #civil #design #bridge

Digital twins began as mirrors of operations, useful but descriptive, reflecting what is rather than letting teams rehearse what should happen. Recent research pushes a step further with semantic twins that encode rules, constraints, and relationships directly from unstructured text into executable knowledge graphs. In one case study, LLMs extract regulatory and design constraints, formalise them as RDF, and drive simulations that stay compliant as conditions change. This shift is profound beyond infrastructure. When policy, process, and risk become machine-readable, you can preview choices and see consequences before spending or risking anything. Without a semantic layer, a twin is another dashboard, descriptive rather than decisive. Add semantics, and it becomes a rehearsal space for judgment, where agents on rails explore scenarios safely and every action leaves an auditable trail. This is how we move from app silos to workflows, from diagrams to living processes, and from demos to state change backed by evidence. I keep returning to a simple claim that feels increasingly obvious in practice: preview first, then build, because simulated failure is cheaper than real-world failure. A good twin lets AI discover better flows, turns processes into living, queryable objects, and makes innovation routine by eliminating downside risk. If agents are workflows that act, remember, and spend, then semantic twins are the rails that keep them aligned with policy, context, and outcomes. This research even shows regulation-aware optimisation and hurricane simulations expressed as RDF states, each operational change traceable and testable later. Over the next few months I’ll be writing more about digital twins, semantics, and receipts, because the architecture is finally catching up with the promise. I know that because I’m watching it being built by the chap at the front of that promise.

𝗧𝗵𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝘄𝗶𝗻 𝗜𝘀 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 — 𝗮𝗻𝗱 𝗪𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗜𝘀 𝗡𝗼 𝗟𝗼𝗻𝗴𝗲𝗿 𝗘𝗻𝗼𝘂𝗴𝗵 For years, Digital Twins were positioned as the pinnacle of smart manufacturing. Accurate simulations. Predictive insights. Impressive dashboards. But there was a quiet limitation: most twins could observe change, not keep up with it. They reported problems after they surfaced. In systems that never stabilize, that delay matters. Early Digital Twins mirrored physical systems for design and planning. Then IoT, sensors, and analytics connected them to real-time operations. Factories became more connected, more automated, more complex. Decision-making didn’t scale at the same pace. That pressure led to the Cognitive Twin. Cognitive Twins don’t just simulate — they reason. They learn from data, select the right models at the right moment, and explain why issues are emerging, not just when. At a Tier-1 automotive supplier, cognitive twins reduced unplanned downtime by 17% across multiple assembly lines by identifying failure patterns earlier than rule-based systems. Still, cognition alone isn’t sufficient. Products change mid-lifecycle. Lines are reconfigured. Human behavior remains dynamic. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗧𝘄𝗶𝗻𝘀 𝗲𝗺𝗲𝗿𝗴𝗲. Adaptive Twins evolve alongside the physical system itself. They continuously recalibrate as machines, workflows, and people change — enabled by edge computing and distributed learning. Edge-based control consistently cuts latency and accelerates control loops — foundational for adaptive digital twins. Humans are now modeled within the system. Behavioral signals such as operator fatigue patterns are captured to dynamically adjust collaborative robot speed and task allocation in real time. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗻𝗼𝘄 𝗹𝗼𝗼𝗸𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: Problems addressed before alarms fire. Operators guided, not overwhelmed. Factories that grow more capable with age. Digital Twins reflected reality. Cognitive Twins understood it. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗧𝘄𝗶𝗻𝘀 𝘀𝗵𝗮𝗽𝗲 𝗶𝘁.

The Digital Twin of the Production System: A Key to Modern Manufacturing. Let’s think about the factory as a big complex machine. A machine that will outlive the products it produces. Would you develop such a machine without creating a digital Model? The digital twin of a factory is a virtual, real-time replica of its physical counterpart. This isn't just a static 3D model; it's a dynamic, living simulation that utilizes data from sensors, IoT devices, and other sources to accurately replicate the actual factory's operations, processes, and performance. This technology is essential because it allows manufacturers to run "what-if" scenarios without halting real production or wasting resources. It creates a risk-free environment for testing new ideas, optimizing processes, and identifying potential problems before they can cause costly disruptions. The result is a more efficient, agile, and sustainable operation. How Siemens Empowers the Factory Digital Twin Siemens is a leader in this field, helping its customers develop and sustain their digital twins through its comprehensive Digital Enterprise portfolio. The company's approach isn't limited to a single product; it's a holistic ecosystem that integrates the entire product and production lifecycle. Here's how Siemens helps: Designing and Simulating: Siemens' software, such as the Xcelerator platform, enables companies to create a digital twin from the outset. This includes developing products, planning production lines, and simulating factory layouts to ensure everything is optimized before any physical assets are purchased. Connecting the Physical and Digital: Siemens provides the automation and industrial IoT technology to collect real-time data from the factory floor. This constant stream of information ensures the digital twin is always an accurate, up-to-date reflection of the physical factory, enabling real-time monitoring and predictive analytics. Long-Term Maintenance and Optimization: A digital twin is an ongoing project, not a one-time build. Siemens provides the tools and expertise to maintain the twin over its entire lifecycle. The company's solutions enable continuous data analysis, identify areas for improvement, and simulate changes to support the factory's peak performance for years to come. Siemens' comprehensive digital twin enables manufacturers to significantly reduce time-to-market, improve product quality, and increase overall efficiency. It's a game-changer for businesses looking to stay competitive in the era of Industry 4.0. For example, here is a diagram of a Battery production system. Here we achieved: 20% reduction in space, 30% improvement in productivity, and 25% faster material replenishment.

#Digital_twins are emerging as a transformative tool in modernizing randomized clinical trials (#RCT). This paper by Hossein Akbarialiabad and colleagues illustrates how digital twins can enhance evidence generation: 1. Virtual patient generation: AI models combine clinical, imaging, genomic, lifestyle, and historical trial data to create synthetic patient profiles that reflect real-world diversity, moving beyond the narrow slices typically enrolled in trials. 2. Simulation of virtual cohorts: Digital twins can act as synthetic controls or virtual treatment recipients, minimizing placebo exposure, reducing sample sizes, and allowing in-silico exploration of safety and efficacy prior to involving real patients. 3. Predictive modeling and optimization: Adaptive designs, dose optimization, SHAP-based interpretability, and continuous model refinement contribute to smarter, faster, and more transparent trials. Encouragingly, real-world applications are already demonstrating significant impacts: - In cardiology, the inEurHeart RCT utilized a cardiac digital twin for ventricular tachycardia ablation, resulting in 60% shorter procedures and 15% higher acute success rates. - In diabetes, a digital-twin-powered assistant in a 12-week RCT for older adults with type 2 diabetes lowered HbA1c by 0.48%, reduced mental distress, and improved self-care adherence. - In oncology, digital twins that integrate tumor-growth models with imaging are personalizing therapy and simulating treatment responses, advancing precision oncology. - In drug development, digital twins facilitate in-silico trials and early safety assessments, accelerating discovery, reducing reliance on animal studies, and enhancing early-phase decision-making. While digital twins show real promise, their impact will depend on rigorous validation, transparent methods, strong privacy safeguards, and thoughtful regulatory pathways. They won’t replace RCTs, but can meaningfully strengthen them, making evidence generation more efficient, inclusive, and patient‑centered. Interested readers may refer to the attached paper below for more details and share your comments.

In my original post, I outlined five shifts shaping the evolution of GIS in the AI era. I then explored Geospatial Foundation Models as the technological engine, and Conversational GIS and Spatial RAG as the interface layer democratizing access to spatial intelligence. Today, I want to focus on the third shift: Predictive Digital Twins. ◉ From visualization to simulation Digital twins are not new. Many cities, utilities, airports, and campuses already maintain 3D models of their assets and environments. What is changing is their purpose. With AI integrated into GIS platforms, digital twins are evolving from static representations into predictive simulation environments. They no longer just show what exists. They help anticipate what could happen next. ◉ What makes a digital twin predictive? A predictive digital twin fuses multiple layers: Authoritative GIS data Building and infrastructure models Real time IoT and sensor feeds Climate projections and risk layers AI driven simulation and pattern detection This combination allows leaders to run forward looking scenarios, not just visualize current conditions. An urban planner can simulate the impact of a new transit corridor on congestion patterns and land use over time. A coastal city can model how different sea level rise scenarios will affect specific neighborhoods and infrastructure assets. An energy provider can test how grid performance responds to extreme heat combined with peak demand. ◉ Why this matters strategically Capital allocation decisions are long term and expensive. Infrastructure, transport, utilities, and climate resilience projects often shape communities for decades. Predictive digital twins allow organizations to test assumptions before committing resources in the physical world. They reduce uncertainty and improve risk management by making complex system interactions visible and measurable. ◉ The role of GIS At the core of every meaningful digital twin is a robust geospatial foundation. Location provides the organizing framework that connects assets, demographics, environmental variables, and risk models. Without a strong GIS architecture, a digital twin becomes a 3D visualization tool. With it, it becomes a decision platform. From where I sit, predictive digital twins represent the convergence of GIS, AI, and operational systems into a single strategic capability. They move spatial technology from descriptive insight to anticipatory intelligence. In the next post, I will explore the fourth shift: Edge Intelligence and Autonomous Updates.

Most engineers still use simulation primarily as the only validation tool. I believe this approach is becoming outdated. In recent work, I have been examining the shift toward AI-assisted digital twins. A consistent challenge emerges: traditional process models (Aspen Plus, HYSYS, DWSIM) are often too static and insufficiently connected to real-time plant data. While they perform well under steady-state assumptions, their relevance declines when exposed to operational variability—feed fluctuations, fouling, or catalyst deactivation. This limitation has historically reduced their value in live decision-making. Earlier digital twin implementations attempted to address this gap but often fell short. Many focused on visualization rather than actionable insight, delivering dashboards without predictive or optimization capabilities. However, recent developments indicate a more effective approach. By integrating process simulation with real-time data and AI-driven surrogate models, engineers can significantly reduce computational time while preserving the rigor of first-principles models. This has influenced how I approach simulation. I no longer see it as a standalone exercise, but as foundational engines for building scalable operational system. Instead of repeatedly running sensitivity analyses, I can leverage faster predictive layers while maintaining a physics-based foundation. Compared to conventional workflows—where simulation was largely confined to design stages—current practices are evolving toward integrated systems that support operations, maintenance, and strategic planning. This shift also introduces scalability. Across hydrogen, refining, biomass, and CCUS applications, such systems enable improved efficiency, cost control, and emissions reduction at scale. The implication is clear: the role of the engineer is evolving. It is no longer sufficient to build accurate models. Increasingly, value lies in the ability to integrate physics-based simulation with data and AI into cohesive, scalable solutions. #ChemicalEngineering #ProcessSimulation #DigitalTwin #AI #EnergyTransition #Industry40