Wake up! Construction 4.0 Is Here and Embraces Safety Innovation

whitePaper | February 24, 2022

Cloud-native engineering empowers organizations to design and develop scalable, agile, cost-effective applications and run them in dynamic environments such as on-premises or in public, private, or hybrid clouds. A cloud-native application consists of microservices working together in an application that can be scaled easily. These microservices are often packaged in code along with libraries and dependencies to run anywhere. In a cloud-native app, the application code is designed, built, and delivered very differently than it would be for conventional, monolithic apps. Cloud-native engineering enables loosely coupled systems that are resilient, manageable, and observable to be combined with robust automation, allowing engineers to make changes frequently.

whitePaper | October 17, 2022

Engineering calculations are at the heart of civil engineering design. The calculation sheets that accompany a project can provide evidence of the design competency and that the specifications are met. Common building calculations are formalized in design guides, such as Eurocodes, AASHTO, IBC etc. The equations tend to be mathematically simple – only needing basic arithmetic operations on the design parameters – but they still require engineering experience to determine the which, why, and when.

whitePaper | May 1, 2021

Technology enabling data capture and analysis, connectivity, monitoring and control is becoming the new baseline for smart buildings of tomorrow. Understanding what it is and where it’s headed is critical.

whitePaper | April 30, 2021

This is the third in a series of white papers by Wavin and New Civil Engineer looking at how sustainable drainage systems (SuDS) can reduce damage and cost caused by floods in the UK.

whitePaper | May 12, 2022

Machine learning is powering most of the recent advancements in artificial intelligence including autonomous systems, computer vision, natural language processing, predictive analytics, and a wide range of applications among the seven patterns of AI. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant amounts of data to be able to create models with acceptable levels of accuracy.

whitePaper | February 10, 2020

Engineers are facing ever-increasing pressure from business executives to incorporate innovative technologies into their products. The realities of creating new and unexplored concepts, while accounting for more and more smart connected products, and continually adapting to changing market uncertainties, is making it challenging for companies to compete effectively.