From optimizing road maintenance to the smart management of urban spaces, including energy efficiency in large-scale infrastructure and the improvement of bidding and procurement processes, Sacyr is driving solutions based on data, machine learning, predictive models, and generative artificial intelligence that enable the company to anticipate issues, make better decisions, and generate a positive and measurable impact on society.
Here are some of the most significant projects in which the company is applying artificial intelligence in a tangible way.
1. APROMAC: Artificial Intelligence to Anticipate Pavement Deterioration
Sacyr Concesiones is developing APROMAC, an advanced predictive analytics module designed to transform pavement management and maintenance through machine learning and deep learning techniques. The project’s main objective is to anticipate changes in key indicators of pavement condition—such as ruts, macrotexture, and the transverse friction coefficient (TFC)—based on real-world operational and road inspection data.
APROMAC was developed in response to the limitations of traditional prediction models, which are based on empirical formulations that struggle to represent nonlinear behavior and the simultaneous interaction of multiple factors. To address this, the project systematically investigates the construction, climatic, and traffic variables that influence pavement deterioration, applying big data and artificial intelligence technologies to real historical data from roads managed by Sacyr.
Following a rigorous data cleaning and standardization phase, new predictive models have been trained and validated that are capable of significantly improving accuracy compared to current approaches. The result is a tool that enables long-term prediction of pavement behavior and supports more efficient maintenance planning.
The project also relies on a digital platform designed for infrastructure managers, which facilitates the visualization of projected trends by homogeneous sections and the analysis of different maintenance scenarios, promoting proactive, data-driven decision-making. APROMAC is co-funded by the European Union, the Ministry of Finance, and the CDTI, through the Ministry of Science, Innovation, and Universities.
2. SUSTAIN: Digital Twins for Smarter, More Human-Centered Urban Management. The Example of “Las Setas de Sevilla”
The SUSTAIN project explores the potential of digital twins and artificial intelligence in the smart management of unique urban spaces. Within this framework, Sacyr has developed an advanced digital twin of Las Setas de Sevilla, an iconic landmark that combines a public square, a market, a viewpoint, and access to an archaeological site.
The solution combines real-time data from IoT sensors, computer vision systems, and mobility data to create a dynamic digital representation of the site and how users interact with the space. Thanks to this information, the digital twin provides insights into usage patterns, environmental conditions, and visitor volumes, as well as the ability to simulate different event scenarios and configurations of the public space.
Using predictive models based on artificial intelligence, SUSTAIN is able to anticipate peaks in demand, visitor behavior, and operational needs, tangibly improving the efficiency of management, planning, and the citizen experience. The project also incorporates indicators related to the condition and maintenance of the infrastructure and is designed as a tool for designing and evaluating urban improvement strategies with a comprehensive approach.
Universal accessibility, inclusion, quality of life, and integration into the urban fabric are cross-cutting themes of the project, aligned with Sacyr’s vision of developing infrastructure with a positive social impact. The initiative, funded by the CDTI with Next Generation funds from the European Union, lays the groundwork for scaling these solutions to other concession assets such as transit hubs, airports, hospitals, and large public spaces.
3. Artificial Intelligence to Optimize Energy Efficiency at Madrid’s Transit Interchanges
Sacyr Concesiones has implemented an advanced energy optimization solution based on artificial intelligence at the Moncloa and Plaza Elíptica transit interchanges, high-traffic infrastructure facilities that together serve more than 260,000 users daily.
Both transit hubs feature complex HVAC and ventilation systems, managed through building management systems (BMS), and are subject to a highly demanding concession framework, with requirements for thermal comfort and environmental quality throughout their operation. To optimize their operation, Sacyr has integrated the Respira solution, developed by Sener, which acts as an autonomous virtual operator supported by advanced analytics and AI.
The system continuously analyzes variables such as indoor and outdoor temperatures, the building’s thermal inertia, occupancy, weather conditions, and historical equipment performance. Unlike traditional models based on rules and reactive responses, the solution introduces predictive control capable of anticipating temperature changes in the spaces and activating the HVAC systems more efficiently.
Thanks to this approach, it is possible to take advantage of favorable environmental conditions, optimize the use of thermal inertia, or adapt climate control to anticipated changes in occupancy, thereby reducing energy consumption without compromising user comfort. The result is a more sustainable and efficient operation that is aligned with infrastructure decarbonization goals.
4. AI Agents to Transform Bidding and Procurement Processes
Beyond physical assets, Sacyr is applying artificial intelligence to its corporate processes, with a special focus on bidding and procurement—highly complex and document-intensive areas. The company is working on the development of a suite of AI agents that comprehensively cover the entire process cycle.
In the area of bidding, the approach is based on an ecosystem of specialized agents capable of automatically analyzing bid documents, detecting changes and addenda, structuring requirements, supporting strategic go/no-go decisions, generating technical documentation, and strengthening quality controls prior to the submission of bids. The goal is to move toward more standardized, traceable, and robust decision-making processes.
In the area of purchasing and procurement, AI agents enable the standardization of tender documents and requests for proposals, the analysis of suppliers, the objective comparison of proposals, the detection of contractual deviations, and support for negotiations. The process is rounded out by the generation of clear executive summaries that can be defended in the face of audits and before management.
These solutions are designed to act as co-pilots that enhance the work of teams by eliminating repetitive tasks and reducing ambiguity, allowing professionals to focus on strategic analysis and value creation.
