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How DAARWIN Can Enhance the Sustainability of Construction Projects?

Updated: Feb 13

Society today demands sustainable, efficient, and safe cities and infrastructures. Throughout time, engineers and architects have faced numerous challenges in designing and constructing such projects. While they have overcome many, ground uncertainty remains a significant issue, leading to over-dimensioning, delays, and overall safety concerns. To minimize ground uncertainty, SAALG Geomechanics has developed DAARWIN, the first and only software that integrates and analyzes predictive ground models, monitoring data, construction progress, and historical information within a digital space.

Data Connectivity for Informed Decision-Making:

DAARWIN enables seamless connectivity among all project stakeholders, empowering them to make informed decisions for planning, delivering, and managing more sustainable, efficient, and safer projects. This web-based platform offers a comprehensive solution by connecting and centralizing geotechnical data throughout the project's lifecycle. From initial exploration using historical information to correlation with real-time observation data during construction, DAARWIN analyzes these datasets to optimize construction procedures by reducing geotechnical uncertainties.

Harnessing Machine Learning for Sustainability:

A notable feature of DAARWIN is its machine learning algorithm, which enhances the accuracy of predictive models in diagnosing over-dimensioning and unforeseen events. This technology minimizes risks and maximizes sustainability and profitability in construction projects. By reducing over-dimensioning, material consumption and CO2 emissions can be minimized, contributing to environmental preservation and climate change mitigation.

Digitalization of the Project Lifecycle:

Another key benefit of DAARWIN is its ability to digitize the entire project lifecycle, facilitating faster and easier data-driven decision-making. Over the past two decades, the construction industry has seen an average annual growth of 1% in efficiency and productivity, compared to the total economy's growth rate of 2.8%. By integrating numerical models, construction progress, monitoring data, images, and historical information, professionals can analyze multiple design options and ground parameter scenarios, ranging from pessimistic to optimistic, to determine the most optimal design and influential geotechnical parameters.

Design validation and prediction of ground behavior:

DAARWIN enables project stakeholders to compare the designed project with monitoring data, ensuring that construction aligns with the intended plan. Additionally, numerical models can be calibrated to predict real ground behavior, minimizing construction material usage (thus reducing CO2 emissions), avoiding delays, and preventing accidents in projects.


The DAARWIN platform provides a comprehensive solution to address geotechnical challenges in construction projects. By connecting and centralizing geotechnical data, it offers reliable and up-to-date information to all stakeholders, facilitating informed decision-making and optimizing construction procedures. Leveraging machine learning algorithms, DAARWIN improves the accuracy of predictive models, reducing over-dimensioning and mitigating geotechnical uncertainties. This not only enhances project efficiency and profitability but also promotes sustainability by reducing material consumption and CO2 emissions.

In summary, DAARWIN is a powerful tool that enhances the sustainability of construction projects by minimizing over-dimensioning, optimizing construction procedures, and maximizing efficiency. With its ability to digitize the project lifecycle and analyze real-time data, DAARWIN provides a solid foundation for data-driven decision-making and risk management. By embracing this innovative platform, geotechnical professionals can achieve more sustainable, efficient, and safer projects, meeting the expectations of today's society. geotechnical, software engineer, geotechnical engineering software, construction AI, civil engineering software

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