Department of Civil Engineering

Transforming construction with data-driven intelligent processes (AIXCon) research project (4/2022-3/2024)

The project, funded by Business Finland, aims at systemic change in the construction industry by developing new data-driven methods and tools that enable intelligent and continuously learning processes in the construction ecosystem. These data-driven methods and tools leverage multiple data streams, such as environmental, feedback, status, cost, schedule and quality data, which are collected and shared among multiple actors in the construction value chain.

Three consortium companies (Trimble, Flow Technologies, Celsa Steel) and two research institutes (Aalto University, VTT) together with their Finnish and international network partners are innovating methods and tools. Four use cases are considered:
1) AI-based structural design processes;
2) Optimised design, detailing, manufacturing, delivery and assembly processes for concrete reinforcement;
3) Data-driven methods for project management and design management in construction projects; and
4) Data-driven generic and collaborative methods and tools for construction workflow and process optimisation.

The methods and tools developed in AIXCon will use machine learning and system dynamics models to support and automate optimised decision-making and processes. The technologies developed will enable continuous improvement of construction products and design, manufacturing, delivery and assembly processes over time, supporting the growth of competitive advantage for the consortium partners over time as methods and tools are continuously enriched and improved with new data streams.

The AIXCon study is based on business collaboration, international benchmarking, the adoption of best practices from other sectors, and changes in practices made possible by technology and digitalisation. The competitive advantage generated by the methods and tools developed will be reflected in a number of metrics, including:

  • Lower CO2 emissions from construction and higher material and resource efficiency;
  • better cost-effectiveness;
  • faster and more accurate schedules; and
  • better product and process quality for customers, end-users and workers.

Project KPIs

The project will develop intelligent and machine-learning methods and tools based on distributed feedback, impact and situational awareness to optimise, automate and continuously improve construction project processes (Figure 1). The success of the project will be measured by the following key performance indicators:

    KPI 1: Four piloted methods based on shared data, developed for use cases by project partners and validated in international markets. Piloted means that the developed method or tool has proven to be feasible from a technical, business and process user perspective. The added value and configuration needs of the methods for process stakeholders in both domestic and international markets have been validated.
    KPI 2: Development and publication of generic design guidelines, looking at (a) the use of environmental impact data, (b) the transformation of the user experience, and (c) the restructuring of the business. The design guidelines will contribute to the development of methodologies suitable for the consortium's use cases and will also serve other Finnish construction companies that are internationalising and seeking to transform their processes and business through smart and learning data-driven processes.

Figure 1: The transformation of construction processes based on shared data

The project's impact on business growth and exports

The project will generate significant new international business for the participating companies and their Finnish networks through the project's partner-specific and joint development and research work.

The new exports generated by the project will be based both on the competitive advantage created by data-driven methods and on new services and products based on these methods. Exports will include a) construction services and products offered by the consortium companies to international markets and b) scalable IT-based solutions and services that support the business of the consortium companies' B2B customers in international construction markets.

The research institutes involved in the project will contribute to the development of the consortium companies' methods by producing critical new knowledge on a) the use of environmental impact data in smart processes, b) the consideration of user experience in the design of tools, c) the maximisation of the business potential of methods in the construction ecosystem in Finland and abroad, and d) data-based solutions linked to building data models.

Project partners

The project consortium consists of a multidisciplinary research team from Aalto University and VTT and three companies, all with the common goal of researching and developing data-based methods for the continuous improvement of design, manufacturing and construction processes. Trimble, Flow Technologies, Celsa Steel, Aalto University and VTT will receive Business Finland funding.

The organisations represent different actors in the construction ecosystem. They are all needed to collect and share situational, impact and feedback data generated in value chains in order to develop data-driven AI and optimisation-based methods and tools. The organisations and their partners will represent both the developers and end-users of the tools and methods, ensuring that the tools developed are both functional and scalable to international markets. Among the corporate partners, Celsa Steel represents the international manufacturer and end-user organisations of the methods it develops, while Trimble and Flow Technologies focus strongly on developing scalable data-driven B2B business through services and products. Aalto University and VTT are supporting the consortium's development work with research in the areas where there are the most open questions and new joint knowledge development. These are 1) management and use of environmental impact data as part of intelligent processes (VTT), 2) transformation and internationalisation of business models through data-driven methods (Aalto CIV), 3) user experience requirements in the development of methods and tools (Aalto CS), and 4) linking data-driven learning and processes to construction data models (Aalto MEC).

In addition to the formal project partners, the project will use networks and ecosystems of partners to collect data, define requirements and test the validity of solutions. The project will also work closely with ACTOR. ACTOR focuses on on-site data collection and worker situational awareness, while AIXCon uses some of the same data to optimise off-site processes.

Project work packages, responsible persons and planned schedule

The AIXCon project is led by Professor Antti Peltokorpi, Aalto University. The work packages and the people responsible for them are listed below.

  • WP1 Baseline and data sources.
  • WP2 New data sources and data collection methods: Markku Kiviniemi, VTT; Jukka Suomi, Trimble.
  • WP3 Data-driven workflow transformation: Tomi Pitkäranta, Sitedrive (Flow Technologies); Jukka Suomi Trimble.
  • WP4 Data-driven optimization and decision making: Jukka Suomi, Trimble.
  • WP5 User experience transformation: Marko Nieminen, Aalto University; Jukka Suomi, Trimble.
  • WP6 Business transformation: Antti Ainamo, Aalto University.
  • WP7 Coordination and dissemination of results: Antti Peltokorpi, Risto Lahdelma and Marko Nieminen, Aalto University.

Project schedule:


Contact details of AIXCon researchers

Antti Peltokorpi

Associate Professor

Antti Ainamo

Project Researcher
 Henri Pitkänen

Henri Pitkänen

Doctoral Researcher
 Rita Lavikka

Rita Lavikka

Senior Scientist
 Markku Kiviniemi

Markku Kiviniemi

Senior Scientist
Celsa steel service
Business Finland
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