Data Analysis Techniques For Engineers

"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

Understanding Material Microstructures through Monte Carlo Simulation of Grain Growth In materials science, the microstructure of a material significantly influences its properties. One critical phenomenon that shapes these microstructures is grain growth—a process where grains within a polycrystalline material coarsen over time, driven by the reduction of surface energy at grain boundaries. What’s fascinating is how a simple set of rules in this simulation can accurately replicate grain boundary dynamics—similar to Conway’s Game of Life, where basic rules governing cell behavior give rise to surprisingly complex patterns. In grain growth simulations, the process is driven by calculating the free energy of each atom in a lattice based on its current crystallographic orientation and comparing it to a random alternative. If the new orientation results in lower or equal energy, it replaces the former, mimicking the natural tendency of materials to minimize surface energy. Despite its simplicity, this approach effectively captures the intricate process of grain coarsening, demonstrating how elegant mathematical models can unravel real-world complexities. One aspect I’m particularly excited about is how this implementation can simulate the evolution of a cold-worked structure during the annealing process. By initializing the simulation with elongated and oriented grains—representative of cold-worked materials—this model reveals how grains gradually recrystallize and coarsen over time, eventually leading to a more equiaxed microstructure. For those curious and intrigued by this simulation, you can try it yourself! Head over to https://lnkd.in/d2Qrd6yK, where I’ve shared the implementation along with detailed instructions. Dive in, experiment, and explore how grain structures evolve right on your own system!

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

AI/ML for Engineers – Learning Pathway, Part 2 (Datasets, Code, Projects & Libraries for CAE & Simulation) If you're a mechanical or aerospace engineer diving into ML, you’ve probably realized this: There's no shortage of ML tutorials but very few tailored to simulation, CFD, or physics-based modeling. This second part of Justin Hodges, PhD's blog fills that gap. In the blog, you will find: ➡️ Which datasets actually matter in CAE applications. ➡️ Beginner-friendly vs. advanced datasets for meaningful projects. Links to real engineering data like: ➡️ AhmedML, WindsorML, DrivaerML (31TB of aero simulation data) ➡️ NASA Turbulence Modeling Challenge Cases (with goals for ML-based prediction) ➡️ Johns Hopkins Turbulence Databases ➡️ Stanford CTR DNS datasets, MegaFlow2D, Vreman Research, and more He also points to coding libraries, open-source projects, and suggestions for portfolio-building Especially helpful if you're not publishing papers or attending conferences. Read the full blog here: https://lnkd.in/ggT72HiC Image Source: A Python learning roadmap suggested by Maksym Kalaidov 🇺🇦 in CAE applications! He is a great expert to follow in the space of ML surrogates for engineering simulation. #mechanical #aerospace #automotive #cfd #machinelearning #datascience #ai #ml

Risk Assessment in Geotechnical Engineering Key Researchers: Jinsong Huang, Richard Kelly, Andrei Lyamin INTRODUCTION It is estimated that 20% to 50% of construction projects have time and/or cost over-runs and an important contributing factor is unforseen or variable ground conditions. Uncertainty can be reduced through extensive site investigation, but the amount of work required to considerably reduce uncertainty can be uneconomic. Uncertainty can be managed through conservative design or by providing a large contingency to a project, both of which drive up costs. It is possible that many apparently successful geotechnical projects may have cost significantly more than necessary. Thus there is a compelling need for geotechnical engineers to better quantify uncertainty and its consequences in order to improve project outcomes, particularly for large infrastructure projects. QUANTITAITVE RISK ASSESSMENT The evaluation of the safety of geotechnical projects is calculated using analytical or numerical methods and quantified via a safety factor, to keep the probability of failure to acceptable levels. The factor of safety itself can be overly conservative in some cases. It is common to use the same safety factor for different types of applications, without regard to the degree of uncertainty involved in its calculation. Through regulation or tradition, the same safety factor is often applied to conditions that involve widely varying degrees of uncertainty. This approach does not account for the consequences of variability or failure. High probability events attracting a high factor of safety may have negligible consequences, and could be designed more economically. The deterministic approach also does not necessarily reflect the risk appetite of the client or contracto,r and does not allow them to make informed decisions. In the past few years, a number of probabilistic methods, where risk is explicitly quantified, have been developed in the PRCGSE. In particular, Huang et al. (2013) developed a quantitative risk assessment framework, where failures and consequences are assessed explicitly through Monte Carlo simulations. Figure 1 shows a three dimensional slope where both failure and consequence are simulated numerically. In addition, a number of probabilistic approaches have been proposed to assess the influence of spatial variability on the stability of geotechnical structures. Amongst such methods, the Random Finite Element Method (RFEM) is very promising and has been used extensively in recent times. RFEM combines random field theory with the non-linear finite element method (FEM). Recently, researchers in the CGSE have combined the finite element limit analysis (FELA) method with random field theory to predict the collapse state of geotechnical structures in a direct and cost-effective manner.

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

🚀 Scientific Machine Learning: The Revolution of Computational Science with AI In recent years, we have seen impressive advances in Machine Learning (ML), but when it comes to scientific and engineering problems, a critical challenge remains: limited data and complex physical models. This is where Scientific Machine Learning (SciML) comes in—a field that combines machine learning with physics-based modeling to create more robust, interpretable, and efficient solutions. 🔹 Why isn’t traditional ML enough? Neural networks and statistical models are great at detecting patterns in large datasets, but many scientific phenomena have limited data or follow fundamental laws, such as the Navier-Stokes equations in fluid dynamics or Schrödinger’s equation in quantum mechanics. Training a purely data-driven model, without physical knowledge, can lead to inaccurate or physically inconsistent predictions. 🔹 What makes SciML different? SciML bridges data-driven models with partial differential equations (PDEs), physical laws, and structural knowledge, creating hybrid approaches that are more reliable. A classic example is Physics-Informed Neural Networks (PINNs), which embed differential equations directly into the loss function of the neural network. This allows solving complex simulation problems with high accuracy, even when data is scarce. 🔹 Real-world applications where SciML is already transforming science: ✅ Climate & Environment: Hybrid deep learning + atmospheric equations improve climate predictions. ✅ Engineering & Physics: Neural networks accelerate computational simulations in structural mechanics and fluid dynamics. ✅ Healthcare & Biotechnology: Simulations of molecular interactions for drug discovery. ✅ Energy & Sustainability: Optimized modeling of nuclear reactors and next-generation batteries. 🔹 Challenges and the future of SciML We still face issues such as high computational costs, training stability, and the pursuit of more interpretable models. However, as we continue to integrate deep learning with scientific principles, the potential of SciML to transform multiple fields is immense. 💡 Have you heard about Scientific Machine Learning before? If you work with computational physics, modeling, or applied machine learning, this is one of the most promising fields to explore! 🚀 #SciML #MachineLearning #AI #PhysicsInformed #DeepLearning #ComputationalScience

🤔 Ever wondered you get hard core scientific proof that your correlations and model results aren't just spurious ❓ 🥇 The example here is the gold standard. Let's take the #Tech sector #XLK We have produced a factor model, where XLK returns are a function of macro factor returns like real GDP Nowcasting, inflation, real/nominal rates, credit spreads, the US Dollar. 12 factors in total (with the data all normalized and "de-correlated" using a Partial Least Squares Regression PLSR) 👉 We ran a Null Hypothesis test: A statistical method for determining if a REAL relationship exists in a population, or if an observed relationship in a sample is just due to chance It involves assuming that a “null hypothesis” of “no effect” is true and then using sample data to decide if there is enough evidence to reject it in favour of an alternative hypothesis The test’s outcome helps researchers make inferences about the larger population based on sample data, ensuring statistical rigour and managing the risk of false conclusions For a particular day, given that one has the (historical) factors mean return and their CoVar matrix (125 trading days, 90 half-life) ..and assuming the factor return jointly follows a multivariate Gaussian distribution (or any other distribution like an alpha-stable) ..it is possible to generate (simulate) multivariate random draws of our factor returns that follow that distribution (correlations included). We generate 125 of these simulated random draws in each step (the same as the historical window) Then we take these random generated factor returns and we regress them against the target (e.g. XLK) For this operation (PLSR), we also get the value for the macro exposures. ❗ These exposures were obtained from a random sample, therefore they are the result of chance. 🤖 We repeat the above process 10,000 times and we record those 10,000 exposures (and the R^2) and we do a histogram (in blue) with them This histogram give us the "range" of exposure values one can get from a pure chance process Then we do one extra PLSR this time with the REAL factor return data We plot the real exposures over the previous histograms (red line) ❓ And the question is: Are the red lines (real exposures) well inside the histograms of the random samples or not ? If they are, then those exposures (or R^2) are NOT significant because they could have been obtained just by chance However, when we look at these plots we see that the R^2 are every time very far from the histograms , and many of the model exposures are on the distribution tails (> 95% tail) or much further away One can only conclude that: 1️⃣ Macro is driving XLK: Significant R^2 (outside of the histograms by over 40 std deviations) 2️⃣ Many macro exposures also are significant (outside the histograms), because they couldn't have been the result of chance 👉 A null hypothesis test on your model is a very rigorous way to test for spuriousness #equities #factorinvesting

Why does signal processing math feel hard? It’s not the math. It’s how we learn it. We memorise symbols. We rarely build systems. I was that telecom engineer who froze at complex numbers and stared at Fourier transforms like a foreign script. It clicked only when I taught five students and forced myself to tie every idea to a real signal and a small build. Here’s the 7‑day fix I use with the 1:1:1 method: one concept, one real example, one tiny project. Day 1 — Complex numbers Concept: phasors and Euler’s formula. Example: ECG lead II. Build: compute magnitude/phase of a synthetic ECG in Python and plot. Day 2 — Sampling Concept: Nyquist and aliasing. Example: audio clip at 8 kHz vs 44.1 kHz. Build: resample and hear aliasing artefacts. Day 3 — Discrete Fourier Transform Concept: frequency bins and resolution. Example: EEG alpha (8–12 Hz). Build: FFT on a 10‑second EEG segment, mark the alpha peak. Day 4 — Windows Concept: leakage and window choice. Example: Hamming vs rectangular on a two‑tone signal. Build: compare spectra; note sidelobes. Day 5 — FIR/IIR filters Concept: magnitude/phase, stability, delay. Example: EMG powerline hum at 50 Hz. Build: design a notch and a band‑pass; show before/after. Day 6 — Convolution Concept: LTI view of filtering. Example: moving average on PPG to smooth motion noise. Build: implement conv1d and compare with library output. Day 7 — End‑to‑end mini project Concept: tie it all together. Example: detect resting alpha from EEG or remove 50 Hz hum from EMG. Build: clean the signal, extract one feature, and output a simple decision. Keep it small. One plot, one metric, one decision. That’s enough to turn a formula into a skill. This is how we can go from “FFT makes no sense” to shipping small builds and land roles within six months. If you want my simple roadmap for learning math for signal processing, its below, no need to say “roadmap” in comments. Found this helpful? If yes, 👍 Like and ♻️ reshare with your friends. _________ Join 1.7K+ learners in transforming how we learn follow Shankar J. for more.

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance