BLOG ARCHIVE
APR 2024 - CAMERA CALIBRATION
Visual observations provide tons of information during experiments, but definitely also during operations. You can think about lifting, pile driving, and settlement of structures. Although rarely used in the construction industry, computer vision could provide numerical context to visual observations.
SEP 2023 - STRESS HISTORY ANALYSIS
Fatigue is challenging to account due to the large amount of data processing that is required to perform such analyses. This often results in wrong shortcuts in design projects. This is the blog showcases two important steps: identifying load cycles based on time domain data.
AUG 2023 - PHYSICS & STATISTICS
Statistics can be of great use in understanding when certain physical processes can occur. However, it is an absolute necessity to look at the right test statistic. This blog shows a case study on roughness profiles and displays why just using a piece of measurement equipment can be misleading.
JUL 2023 - FORECASTING
Often statistics is all about understanding datasets. But it would be most desirable if we could forecast the future based on the past. This is not easy, but can provide a lot of value if performed correctly. This blog elaborates on a practical case study on predictive maintenance by means of greasing.
MAY 2023 - EXPECTED VALUES
One thing heard a lot in our industry is the quote: 'This is conservative!' But how do you actually determine conservative input? This blog elaborates on a relatively simple classical concept in statistics which is often misunderstood: expected values.
JAN 2023 - EXTREME VALUE DISTRIBUTION
Designs and operations are often tailor made for extreme conditions as these determine the required strength. Extremes are often determined subjectively or based on previous measurements. This blog explains how to apply generalized extreme value distributions on a design problem.
DEC 2022 - THE STANDARD ERROR
One question is often asked when determining the scope of a test campaign. How much tests should we perform to be sure of the parameter mean and properly capture the variation? A proper understanding of the standard error aids in answering this question in an objective manner.
NOV 2022 - TRUNCATED DISTRIBUTIONS
Actual physics is often forgotten in case of the application of statistics in engineering processes. A good example is the normal distribution which can easily sample impossible values. This blog concerns truncated distributions which prevents it and facilitates more realistic simulations.
SEP 2022 - REGRESSION
Fitting a line to data is performed by engineers on a daily basis. In most cases, this is based on linear regression and the person fitting only needs to click a button. Howver, using regression requires much more nuances, especially when uncertainties and outliers play a role in your input data.
JUN 2022 - RANDOM FIELDS
The final blog on spatial variability discusses methods for simulating it. If one knows the trend, amount and speed of variation, potential situations can be simulated. This can benefit designs (by foreseeing the unforeseen) and operations (by predicting likely upcoming situations).
JAN 2022 - AUTOCORRELATION
The second part of the series on spatial variability is all about how to describe variability over space or time. Two important concepts are elaborated on: autocorrelation and scales of fluctuation.
AUG 2021 - CLUSTERING
This is the first blog in a series on spatial variability. It concerns objectifying soil profile clustering by means of a Principal Component Analysis (PCA) and K-means clustering. Many benefits for construction projects can be achieved.
JUN 2021 - COPULAS
This blog elaborates on Copulas. They are from now on implemented as a possible (beta) analysis in the API. Furthermore, a case study shows why they are usefull. It is advised to read the blog from March 2021 if you are not familiar with joint variability.
MAY 2021 - JOINT VARIABILITY
This blog explores the concept of joint variability and shows the addition of one new application to the API. Joint variability occurs when two variabiles are not independent. It is important to understand, since it can have a large impact on the results of your analysis.
APR 2021 - UNCERTAINTY?
In this blog we explore the definition of uncertainty, which is more complex than it may seem. Furthermore, some updates on the API are shared including soil profiling, partial automation and other stuff upcoming on joint variability!
MAR 2021 - DISTRIBUTIONS
This (first) blog discusses the relevance of fitting a proper probability density function. Why is it so often done wrong? What are the consequences of not doing it right? How can you do it right? Furthermore, the use of the API for serving the analyses is treated.