Author Archives: Diego Cisterna

Object recognition in ground plans

Object recognition in ground plans

Dieser Inhalt wurde auch auf der internationalen Konferenz „AI in AEC“ am 24.03.2021 von Patrick Hemmer (KSRI-KIT) präsentiert.

In recent years, more and more companies in the architecture, engineering and construction industries have become aware of the potential of artificial intelligence (AI) to improve work processes. This trend is being driven by various technological advances, such as the increasing adoption of Building Information Modeling (BIM). To date, however, few medium and large companies have been able to use their data profitably. As in many other industries, employees are uncertain about the impact AI will have on their work and are therefore reluctant to support the upcoming transformation.

Das Konzept der „human-centered artificial intelligence" has become increasingly important in recent years, both in practice and in science. The approach is based on the idea that AI systems should not replace employees but complement and empower them. Especially in the field of architecture, where many work processes are characterized by a large number of repetitive tasks, identifying and assigning these tasks to computers is a promising endeavor. For this reason, it can be assumed that more and more systems based on the combination of human and artificial intelligence will find application in practice in the future.

The process of mass determination requires the manual counting of relevant components. In the area of building operations, the challenge here is to identify objects from floor plans, which are usually only available as rasterized images or printouts (see Figure 1). 

Figure 1: Reduction of manual work through AI applications

To solve this problem and promote quality control, the human-in-the-loop system developed as part of the Smart Design and Construction research project recognizes symbols relevant to users in scanned floor plans to support the design process and simplify matching with building requirements (see Figure 2).

Figure 2: By quantifying prediction uncertainty, the system can communicate to users which symbols need to be verified

Here, a bilateral cooperation between user and system takes place. On the one hand, it supports its users by providing relevant recommendations, but on the other hand, it also offers them the opportunity to contribute their domain knowledge in a targeted manner in order to ultimately contribute to more efficient and reliable mass determination.

In addition to this use case, a large number of development teams are working on solutions for other use cases as part of the research project in order to make a decisive contribution to the digitalization of the construction industry.

A new year - where are we heading?

A new year - where are we heading?

In many studies on artificial intelligence, the construction industry is not listed. The question therefore arises, where does AI stand in construction practice and when will it be used as standard?

Artificial intelligence (AI) applications have become a transformative technology over the past decade and, as a result, are already commonly used in many industries (see Figure 1): leading the way are manufacturing, finance, education, and sales. (Deloitte Research 2019)

Figure 1: Application of AI in various industries (source: Deloitte Research 2019)

Since the construction industry is not listed in the figure, the question arises: where in the figure would one place the construction industry?

Betrachtet man die vier Quadranten in der Abbildung „Transition“, „Developed“, „Germination“ und „Growth“, wäre die Bauwirtschaft nach unserer Meinung in den Startlöchern (Germination) verortet.

Initial applications exist as stand-alone solutions. In a survey conducted at SDaC's third roundtable on Jan. 13, 2021, 197 people participated (see Figure 2). Of these, 48% believe that in less than 5 years AI applications will be used as standard in construction practice. Consequently, more and more AI applications will be piloted and widely applied in construction projects in the coming years.

Figure 2: Results of the survey in the third SDaC RoundTable on the use of AI in construction practice (Source: SDaC).

To get there, Reim et. al (2020) suggests four steps to implementing AI applications in your own organization:

  • Creating an understanding of the potential and challenges of AI in the company.
  • Discussion on changing existing business models and its roles.
  • Alignment of IT strategies with business model strategies, with the aim of promoting new competencies, services and customer segments.
  • Organizational acceptance through pilot projects, the formation of AI teams, training on AI, the development of an appropriate strategy, and internal and external communities for further development.

In order to meet the expectations from Figure 2, there is an increased need for action in construction companies to create an understanding with training and education as well as the transformation of existing structures. 

Where do you see a need for support from, for example, research, policy or consulting? Please feel free to contact us Contact.

These are our goals in 2021:

  • The main functionalities of the platform are realized technically.
  • Initial technical prototypes for the applications will be completed based on the data provided in the consortium.
  • On 23-25.04.2021 our first SDaC Hackathon will take place. Information will follow on our homepage under: https://www.sdac.tech/hackathon. Thus, our goal is to promote innovations for the construction industry.
  • We initiate a DIN Spec for the exchange of information of the supply and value chain of building products.

Sources:

Reim, Wiebke; Åström, Josef; Eriksson, Oliver (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. AI 1, no. 2: 180-191. https://doi.org/10.3390/ai1020011

Deloitte (2019). Global artificial intelligence industry whitepaper. https://www2.deloitte.com/cn/en/pages/technology-media-and-telecommunications/articles/global-ai-development-white-paper.html

A guideline for digitization in the construction industry

A guideline for digitization in the construction industry

What potentials result from digitization? What do these potentials look like when several organizations work on the same result? Which measures need to be taken for a step-by-step implementation?

Digital construction networks

An empirical study on the topic "Digital construction networks - an empirical analysis of incoming goods control on construction sites" was conducted.

Several studies are already analyzing what improvements and challenges can be brought through digitization in general (see Telekom 2019/2020). However, how these can be implemented in concrete terms has not yet been sufficiently scientifically examined. Especially when - as in the construction industry - a large number of project participants are involved.

In a particular example - the process of a concrete delivery from order to billing - only 33.33% of the steps on the construction site are value-adding activities. The remaining 66,66% are related to activities that do not add value but are nevertheless necessary. The waste is caused by media breaks and the associated manual processing work.

From the potentials identified in the analysis, measures for digitalization can be derived. The digitalization of data and its interfaces leads directly to improved value creation along the process chains and thus for the entire construction network involved. As a result, a guideline was developed which, by means of a step-by-step action plan, should simplify the implementation towards digitalized incoming goods control. If you are interested in the complete results, please contact us.

Telekom. 2019/2020. „Digitalisierungsindex Mittelstand: Der digitale Status Quo im Deutschen Baugewerbe.“

KI & Drohnen in der Baurealisierung

AI and drones in construction

Photogrammetry using drones to create intelligent 3D models

Using drones on construction sites for construction progress documentation, surveying, mapping and inspection of hard-to-reach areas

Driven by the rapid spread of smartphone technology in recent years, drone technology has also developed dramatically. A variety of smartphone components and sensors, such as gyroscope, lithium-ion battery, camera sensors and many more are also used in commercial drones or multicopters, as they are also called.

The existing drone technology has already reached a stage where it is well suited for use on construction sites with minor adjustments to the software. Possibilities for the use of drones on construction sites include construction progress documentation, surveying and mapping, viewing of hard-to-reach areas.

The challenge in the deployment of drones is to select the appropriate systems from drones hardware, software and data-analytical tools for the respective application. Artificial intelligence plays a decisive role in this process in order to be able to draw the right conclusions from the often huge amounts of data.

AI for converting image data into intelligent 3D models

Ein Beispiel bei der künstliche Intelligenz bereits heute auf der Baustelle zum Einsatz kommt, ist die digitale Erstellung von Dach- und Fassadenvermessungen. Hier hilft die KI um aus Bilddaten, intelligente 3D Modelle zu erstellen, die standardisiert vermessen werden können. Wie das funktioniert ist in den folgenden drei Schritten zu sehen:

  1. On site, a drone is used to capture images of the building:
2. Using photogrammetry algorithms to create a 3D model:
3. Using edge detection and semantic analysis to create an intelligent 3D CAD model:

On the basis of the 3D CAD model, measurements can be taken, construction-related changes can be made, target/actual comparisons can be made and much more. The intelligent 3D model provides the basis for all project participants to exchange information about the project on a uniform database.

Through the appropriate combination of drones and modern AI methods, workers on the construction site can be supported, processes accelerated and risks on the construction site reduced.

Within the framework of Smart Design and Construction, the project team is working on developing a variety of additional applications for construction companies resulting from the combination of AI & drones.