The Ramal do Agreste is a major water infrastructure system designed to transport water from the SĂŁo Francisco River Integration Project to the Agreste Pernambucano, a semi-arid region in the State of Pernambuco, Brazil.
Developed by the Brazilian Federal Government, the Integration Project was conceived to ensure long-term water security for municipalities across the drought-prone Northeast Region.
As one of the largest waterworks ever undertaken in Brazil, the project received an investment of over USD 200 million. The system includes a complex network of canals, siphons, tunnels, dams, and pipelines, delivering water to more than 2 million people across approximately 70 municipalities where extended droughts pose a serious risk to daily life and development.
TPF led the consortium of companies responsible for supervising the construction of this critical infrastructure, ensuring its successful delivery and long-term impact on regional resilience and sustainability.
The Ramal do Agreste project marked a significant milestone for TPF Engenharia, as it was the first undertaken by the company to incorporate artificial intelligence (AI)—demonstrating the transformative potential of intelligent tools in engineering. The core methodology applied in this project is highly adaptable across various domains, enabling TPF to enhance efficiency, strengthen quality control, and improve traceability across its operations.
As one of Brazil’s most important water infrastructure initiatives, the Ramal do Agreste spans approximately 71 kilometres, distributing water to around 70 municipalities in the Agreste Pernambucano region. This semi-arid area, historically affected by prolonged droughts, now benefits from improved water security for over 2 million residents. TPF Engenharia served as the lead firm in the consortium responsible for supervising the project’s execution.
To support this effort, TPF implemented a comprehensive system combining drone monitoring and artificial intelligence, which included the standardisation of quality control processes through autonomous drone flights, the strategic planning of photographic capture coordinates across supervised sections, and the pre-analysis of images using an integrated AI and machine learning platform. The team also trained algorithms to rapidly detect non-conformities in captured images and developed a custom Python-based programme to evaluate the visuals and automatically generate inspection reports.
This innovative approach not only contributed to the successful delivery of a vital infrastructure project but also laid the foundation for future applications of AI-driven supervision in engineering projects.
Sorry, no records were found. Please adjust your search criteria and try again.
Sorry, unable to load the Maps API.
Techne Engenheiros Consultores
Engeconsult Consultores TĂ©cnicos
Ministry of Regional Development
