Within the Engineering, Procurement, and Construction (EPC) industry, engineers manually create documents based on engineering drawings, which can be time-consuming and prone to human error. For example, the expansion of typical assemblies of instrument items (Instrument Typicals) in Piping and Instrumentation Diagrams (P&IDs) is a labor-intensive task. Each Instrument Typical assembly is depicted in the P&IDs via a simplified representation showing only a subset of the utilized instruments. The expansion activity involves recording all utilized instruments to create an instrument item list document based on the P&IDs for a particular EPC project. Fortunately, Artificial Intelligence (AI) could help to automate this process. In this paper, we propose the first method for automating the process of Instrument Typical expansion in P&IDs. The method utilizes computer vision techniques and domain knowledge rules to extract information about the Instrument Typicals from a project's P&IDs and legend sheets. Subsequently, the extracted information is used to automatically generate the listing of all utilized instruments. The effectiveness of our method is evaluated on P&IDs from large industrial EPC projects, resulting in precision rates exceeding 98% and recall rates surpassing 99%. These results demonstrate the suitability of our method for industrial deployment. The successful application of our method has the potential to reduce engineering costs and increase the efficiency of EPC projects. Furthermore, the method could be adapted for additional applications in the EPC industry, which highlights the method's industrial value.

ABSTRACT Manual analysis of diagrams and legend sheets in engineering projects is time consuming and needs automation. The lack of standardized legend formats complicates creating a general method for automated information extraction. Existing approaches require training and custom rules for each project. This study proposes a novel solution combining optical character recognition with vision language models and multimodal prompt engineering to automate information extraction from diverse legend sheets without training. It integrates legend information with information extracted from diagrams, unlike studies that only focus on diagrams. Our study shows that VLMs, guided by multimodal prompts, can accurately extract information from diverse legend sheets, enabling automatic information extraction in diagrams across engineering projects. We validate our method through a case study involving the extraction of instruments from piping and instrumentation diagrams (P&IDs) and their legends across three projects with varied formats and standards. The proposed method achieved 100% accuracy in legend classification and information extraction, and 99.68% precision and 95.91% recall in generating instrument listings. The results demonstrate the effectiveness of our approach, significantly enhancing the accuracy and efficiency of information extraction from diagrams. This method can be adapted to different legend formats and diagrams, providing a versatile solution for various industries.

The success of any Engineering, Procurement, and Construction (EPC) project depends on the engineering deliverables developed during project execution. An important deliverable is the Line List document, produced by extracting pipeline numbers from Piping and Instrumentation Diagrams (P&IDs). As the creation of this document is time-consuming, the automation of this process could reduce manual engineering work. However, the complexity of the P&IDs renders traditional computer vision approaches unsuitable. Therefore, deep learning text detection could be utilized to achieve this task. This study assessed the applicability of text detection methods for automating pipeline number information extraction in P&IDs. Our findings indicate that the methods previously used to detect text on P&IDs have limitations in accurately capturing the entire line numbers. Furthermore, we propose a line number detection method achieving a recall rate of over 90% on our evaluation data, consisting of P&IDs from diverse industrial projects. Thus, we demonstrate our method's generalizability to different line number formats and its potential for industrial application. Moreover, the proposed method can be adapted to other types of engineering drawings beyond P&IDs. Thus, it could be used in additional applications for digitizing engineering drawings.

Applied Artificial Intelligence (AI) in engineering is gaining significant traction. AI object detection methods can be applied in the engineering industry to extract information from engineering drawings, offering immense benefits to engineers. A promising application of AI in industrial engineering is symbol recognition applied to engineering drawings. However, these drawings often exhibit areas with a high density of symbols, as well as noise in the form of markups, indicating revisions. These factors could cause symbol misclassification or omission, impacting applications reliant on accurate symbol recognition. This study evaluates the accuracy of a symbol recognition model on engineering drawings called Piping and Instrumen-tation Diagrams (P &IDs) exhibiting varying levels of density and markups causing noise. Despite the assumption that density poses a challenge for accurate symbol recognition in engineering drawings, our study reveals that density has no significant impact on recognition performance when a dense detector is employed. In addition, we quantitatively show that markup-induced noise on engineering drawings negatively influences recognition accuracy. Finally, we provide recommendations regarding the applicability of symbol recognition in engineering applications. The study's findings and recommendations apply to any P &IDs, regardless of the standard used, as they were evaluated on various worldwide projects. Moreover, the research not only contributes to the advancement of symbol recognition on P&IDs, but also can be applied to other types of engineering drawings. Thus, it holds the potential for enhancing symbol recognition in various real-world industrial applications and research.

In the Engineering, Procurement, and Construction (EPC) sector, accurate cost estimations during the tendering phase are crucial for maintaining competitiveness, especially with constrained project schedules and rising labor expenses. Typically, these estimations are labor-intensive, relying heavily on manual evaluations of engineering drawings, which are often shared in PDF format due to intellectual property concerns. This study introduces an innovative solution tailored for the energy industry, utilizing Artificial Intelligence (AI) - primarily deep learning (DL) and machine learning (ML) techniques - to streamline material quantity estimation, thereby saving engineering time and costs. Built on empirical data from a large EPC company operating in the energy sector, AI-based product development experiences, and academic research, our approach aims to enhance the efficiency and accuracy of engineering work, promoting better decision-making and resource distribution. While our focus is on enhancing a particular activity within the case company using AI, the method's broader applicability in the EPC sector potentially benefits both industry professionals and researchers. This study not only advances a practical application but also provides valuable insights for those seeking to develop AI -driven solutions across various engineering disciplines.

Visualizing graphs is a challenging task due to the various properties of the underlying relational data. For sparse and small graphs the perceptually most efficient way are node-link diagrams whereas for dense graphs with attached data, adjacency matrices might be the better choice. Since graphs can contain both properties, being globally sparse and locally dense, a combination of several visualizations is beneficial. In this paper we describe a visually and algorithmically scalable approach to provide views and perspectives about graphs as interactively linked node-link as well as adjacency matrix visualizations. The novelty of the technique is that insights like clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the others. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by taking into account several layout and reordering properties in combination as well as different edge properties for the same set of nodes. We illustrate the usefulness of our tool by applying it to graph datasets like co-authorships, co-citations, and a CPAN distribution.