Difference between revisions of "Journal:Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering"

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Next to social science research methods, researchers have relied on the structured analysis of documents and other artifacts. As discussed earlier, Min ''et al.''<ref name="MinARule17" />, for example, have formalized design patterns for the design of theme parks. The study identified the patterns through a structured analysis of design documents existing for different realized theme parks.
Next to social science research methods, researchers have relied on the structured analysis of documents and other artifacts. As discussed earlier, Min ''et al.''<ref name="MinARule17" />, for example, have formalized design patterns for the design of theme parks. The study identified the patterns through a structured analysis of design documents existing for different realized theme parks.


With the advancement of text mining and pattern recognition techniques, recently many studies have been published that use automated methods to extract information from existing design documents and models. This field has developed into a vibrant research area. Automated text mining methods have been applied to formalize engineering knowledge from patents (Govindarajan et al., 2019; Wang and Chen, 2019), identify research trends for building energy savings (Ding et al., 2018), analyze construction site accidents (Zhang et al., 2019), predict construction cost overruns (Williams and Gong, 2014), retrieve CAD drawings (Hsu et al., 2013), or extract best practices from simulation modeling guidelines. (Kestel et al., 2019) Additionally, several graph-based pattern mining methods have been applied to architectural floor plan design (Strug and Ślusarczyk, 2009) or to the automated extraction and formalization of construction process patterns. (Sigalov and König, 2017) Finally, geometric pattern recognition techniques have also been developed to support the aerodynamic design of vehicles. (Graening and Sendhoff, 2014)
With the advancement of text mining and pattern recognition techniques, recently many studies have been published that use automated methods to extract information from existing design documents and models. This field has developed into a vibrant research area. Automated text mining methods have been applied to formalize engineering knowledge from patents<ref name="GovindarajanIntell19">{{cite journal |title=Intelligent collaborative patent mining using excessive topic generation |journal=Advanced Engineering Informatics |author=Govindarajan, U.H.; Trappey, A.J.C.; Trappey, C.V. |volume=42 |at=100955 |year=2019 |doi=10.1016/j.aei.2019.100955}}</ref><ref name="WangANovel19">{{cite journal |title=A novelty detection patent mining approach for analyzing technological opportunities |journal=Advanced Engineering Informatics |author=Wang, J.; Chen, Y.-J. |volume=42 |at=100941 |year=2019 |doi=10.1016/j.aei.2019.100941}}</ref>, identify research trends for building energy savings<ref name="DingBuild18">{{cite journal |title=Building energy savings: Analysis of research trends based on text mining |journal=Automation in Construction |author=Ding, Z.; Li, Z.; Fan, C. |volume=96 |pages=398–410 |year=2018 |doi=10.1016/j.autcon.2018.10.008}}</ref>, analyze construction site accidents<ref name="ZhangConstruct19">{{cite journal |title=Construction site accident analysis using text mining and natural language processing techniques |journal=Automation in Construction |author=Zhang, F.; Fleyeh, H.; Wang, X. et al. |volume=99 |pages=238–48 |year=2019 |doi=10.1016/j.autcon.2018.12.016}}</ref>, predict construction cost overruns<ref name="WilliamsPredict14">{{cite journal |title=Predicting construction cost overruns using text mining, numerical data and ensemble classifiers |journal=Automation in Construction |author=Williams, T.P.; Gong, J. |volume=43 |pages=23–29 |year=2014 |doi=10.1016/j.autcon.2014.02.014}}</ref>, retrieve CAD drawings<ref name="YuContent13">{{cite journal |title=Content-based text mining technique for retrieval of CAD documents |journal=Automation in Construction |author=Yu, W.-D.; Hsu, J.-Y. |volume=31 |pages=65–74 |year=2013 |doi=10.1016/j.autcon.2012.11.037}}</ref>, or extract best practices from simulation modeling guidelines.<ref name="KestelOntol19">{{cite journal |title=Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes |journal=Advanced Engineering Informatics |author=Kestel, P.; Kügler, P.; Zirngibl, C. et al. |volume=39 |pages=292–305 |year=2019 |doi=10.1016/j.aei.2019.02.001}}</ref> Additionally, several graph-based pattern mining methods have been applied to architectural floor plan design<ref name="StrugReason09">{{cite journal |title=Reasoning about designs through frequent patterns mining |journal=Advanced Engineering Informatics |author=Strug, B.; Ślusarczyk, G. |volume=23 |issue=4 |pages=361–69 |year=2009 |doi=10.1016/j.aei.2009.06.007}}</ref> or to the automated extraction and formalization of construction process patterns.<ref name="SigalovRecog17">{{cite journal |title=Recognition of process patterns for BIM-based construction schedules |journal=Advanced Engineering Informatics |author=Sigalov, K.; König, M. |volume=33 |pages=456–72 |year=2017 |doi=10.1016/j.aei.2016.12.003}}</ref> Finally, geometric pattern recognition techniques have also been developed to support the aerodynamic design of vehicles.<ref name="GraeningShape14">{{cite journal |title=Shape mining: A holistic data mining approach for engineering design |journal=Advanced Engineering Informatics |author=Graening, L.; Sendhoff, B. |volume=28 |issue=2 |pages=166–85 |year=2014 |doi=10.1016/j.aei.2014.03.002}}</ref>




Revision as of 18:06, 5 January 2021

Full article title Advanced engineering informatics: Philosophical and methodological foundations
with examples from civil and construction engineering
Journal Developments in the Built Environment
Author(s) Hartmann, Timo; Trappey, Amy
Author affiliation(s) Technische Universität Berlin, National Tsing Hua University
Primary contact timo dot hartmann at tu-berlin dot de
Year published 2020
Volume and issue 4
Article # 100020
DOI 10.1016/j.dibe.2020.100020
ISSN 2666-1659
Distribution license Creative Commons Attribution 4.0 International
Website https://www.sciencedirect.com/science/article/pii/S2666165920300168
Download https://www.sciencedirect.com/science/article/pii/S2666165920300168/pdfft (PDF)
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

We argue that the representation and formalization of complex engineering knowledge is the main aim of inquiries in the scientific field of advanced engineering informatics. We introduce ontology and logic as underlying methods to formalize knowledge. We also suggest that it is important to account for the purpose of engineers and the context they work in while representing and formalizing knowledge. Based on the concepts of ontology, logic, purpose, and context, we discuss different possible research methods and approaches that scholars can use to formalize complex engineering knowledge and to validate whether a specific formalization can support engineers with their complex tasks. On the grounds of this discussion, we suggest that research efforts in advanced engineering should be conducted in a bottom-up manner, closely involving engineering practitioners. We also suggest that researchers make use of social science methods while both eliciting knowledge to formalize and validating that formalized knowledge.

Keywords: advanced engineering informatics, knowledge formalization, knowledge engineering, computing in engineering, research method, engineering

Introduction: Attempting to define advanced engineering informatics

Engineers invent, design, analyze, build, test and maintain complex physical systems, structures, and materials to solve some of societies most urgent problems, but also to improve the quality of life of individuals. Engineering is artifact-centered and concerned with realizing physical products of all shapes, sizes, and functions. Engineers routinely use computers and engineering work is almost entirely digitized. Few tasks are conducted without some sort of digital support. Surprisingly still, some engineering disciplines, and in particular, civil engineers are termed (and term themselves) as digital laggards. Resistance to apply new digital technologies is high, and more often than not the real benefits of applying new digital technologies to support engineering design tasks is not perceived, visible, or existing.

The existing resistance towards adopting advanced computational tools has traditionally been attributed to individual and social characteristics of engineers themselves. For example, traditionally, studies focusing on the work of civil and construction engineers attributed resistance to the organizational characteristics of the industry, such as the seminal study of Mitropoulos and Tatum[1] about general industry characteristics, or the more recent study of Linderoth[2] looking at the specific collaboration network structure of the industry. Others like Davis and Songer[3] have attributed the resistance of engineers to adopt new technologies to individual characteristics of engineers, such as age, gender, general computer understanding, or experience.

Independent of resistance and its cause and despite the ever growing amount of digital applications that are used by engineers, it rather seems as if engineers are increasingly struggling with providing and improving our society’s complex engineering systems.[4] This, in particular, holds in relation to the engineering systems within our built environment. Little research has provided insights into how the characteristics of computational tools influenced adoptions. Those studies that did showed that there seems to be a large difference between the general expectations of the engineers with the support that the tools could truly provide.[5][6] This paradox of supporting today’s engineering work with adequate computational tools has triggered the engineering community to develop a new scientific field of study and inquiry: advanced engineering informatics.

Advanced engineering informatics is motivated by the quest to empower engineers to cope with the ever increasing complexity of the systems they have to provide. The discipline strives to provide means that allow engineers to leverage their understanding of the behavior of complex systems through advanced simulation and data analysis methods. It also strives at improving the collaboration and communication of engineers within the ever more complex collaborative interdisciplinary arrangements they face.

Unlike other related disciplines, advanced engineering informatics focuses not on the automation of mundane tasks, but on developing, researching, and exploring methods to enhance the existing work environment of engineers. Advanced engineering informatics scholars believe that well-designed computational methods have the potential to empower engineers in ways that have previously not been possible. They believe that computers cannot only incrementally speed up engineering design work, but significantly disrupt engineering tasks throughout the entire product development life-cycle, from the early stages of conceptual design, to detailed engineering design, to production, to the maintenance of engineered systems.

To the above end, advanced engineering informatics acknowledges that engineering work is a knowledge-intensive activity.[7] Any research into how computational methods can support engineering work needs to start with an explicit formalization of the knowledge engineers posses. Advanced engineering informatics is a specific discipline of knowledge engineering[8] with an overarching research question: “How can we formalize complex engineering knowledge to develop advanced computational methods that help engineers to solve practical problems within their constraints and budgets?”

With this research question—above and beyond improving our understanding in how to formalize complex engineering knowledge through explicit representations and symbolic or numerical process models—advanced engineering informatics is hence also concerned with understanding how such representations can support practical engineering work. To this end, topics for research are not only the development of advanced computational methods based on explicitly formulated knowledge, but also exploring the representation of information in graphical user interfaces, the provision of extensive knowledge bases through large scale databases, or how engineers and engineering groups can be supported in interpreting solutions and intermediate solution spaces.[7] In all of these endeavors, an explicit focus on engineering knowledge is required to advance this understanding.

Despite the scientific and practical importance, most studies published in the scientific engineering journals fail to explicitly address aspects of engineering knowledge formalization and representation. This also holds for publications focusing on the engineering of our built environment. More often than not, new methods, algorithms, or results of data analysis efforts are presented without the contextualization of the suggested methods within a specific engineering context. Often it is not clear how suggested novel methods make use of explicitly formalized engineering knowledge and how the methods support engineers in their knowledge-intensive tasks. By large, the scientific engineering community still needs to establish a continuous growing body of scientific knowledge about how advanced computational methods can support engineers. Consequently, little general understanding about how novel computation methods can be implemented across tasks and engineering disciplines exists. This lack, in turn, has slowed down the development of solutions that could truly enhance practical engineering work.

This paper is an effort to refocus the current scientific discourse on the importance of engineering knowledge. To this end, we attempt to first provide a clear definition and description of the underlying philosophical basis of knowledge formulation and knowledge engineering as the foundation for all scientific inquiry within the field of advanced engineering informatics. We illustrate these definition and descriptions using a number of recently published articles that focus on the domain of built environment engineering as example.

Our second goal for the paper is to start a discussion about the required methodological approaches for advanced engineering informatics research practice. So far there is little to no discourse about research methods within the field, which has significantly hindered its establishment among the other scientific disciplines. To catalyze this discourse, in the second part of the paper we suggest different research approaches and some underlying theories.

Of course, like every other scientific discipline, the definitions, concepts, methods, and approaches associated with advanced engineering informatics are an ever-moving target. Therefore, this paper can only represent our current reflections and thinking in the field and is intended to provide food for thought and a catalyst for more reflective and vibrant discussion. By no means are the presented concepts of knowledge formalization and research methods meant as fixed bearing points, but rather as points of departure for wider theoretical explorations. Therefore, the paper also provides an elaborated discussion section with suggestions for future important areas of inquiry.

In the next section, we introduce the theoretical underpinnings of knowledge representation and knowledge formalization. That section also illustrates these underpinnings using four recently published research studies. Then different research methods that might be appropriate for advanced engineering informatics research are suggested. Finally, an extensive discussion with suggestions for important research directions are presented, along with conclusions.

Knowledge representation and formalization

Sowa[8] defines knowledge engineering as the application of ontology and logic to the task of building computational models of some domain for some purpose. To inform advanced engineering informatics research, the definition provided in the introduction is informative as it focuses knowledge engineering on two important aspects. First, the definition suggests building computational models. Hence the definition proposes to move beyond the development of mathematical algorithms, towards models that already make computational prediction about a domain. Second, the definition also implies purpose, therefore requiring a focus on solving practical problems. These two aspects are of utmost importance for all research into advanced engineering informatics. The discipline is not concerned with conceiving new mathematical methods, algorithms, and calculation mechanisms, but rather it is concerned with using such basic computational methods to build models that compute tangible results that are relevant for a specific engineer. Furthermore, this relevance needs to be related to a practical engineering purpose within the wider product development cycle of an engineering system.

Moreover, the definition points towards basic methodological approaches that advanced engineering informatics researchers have to be familiar with: ontology, logic, and computation. Within information science, an ontology is a formal representation of all a topic's concepts and their relations. An ontological knowledge representation is concerned with the knowledge of engineers about physical and abstract objects, relations between those objects, and events influencing those objects. Ontological representation allows for a commitment with respect to the model of the specific domain that is required as the basis for any computational method. With this commitment, ontologies help humans and computers understand and fully utilize domain knowledge. One important aspect of advanced engineering informatics research focuses on developing approaches for implementing computer-assisted engineering platforms that apply ontology-based theories and solutions.[9][10]

Each ontology supporting such solutions needs to map the knowledge within a specific universe of discourse.[11] This universe of discourse should be a carefully bounded and focused micro-world[8] within an engineering discipline. Alternatively, it could also focus on a specific engineering collaboration between two engineering disciplines. To arrive at computational models as defined above, a bottom-up approach that focuses on a very specific engineering task is required. Moreover, domain ontology schema should be built and updated constantly together with all stakeholders of the knowledge domain. Knowledge is dynamically changing and growing, and, most importantly, it is possessed by multiple domain experts.[12]

The second methodological approach that is suggested by Sowa’s definition is logic. Logic is the systematic study of inference that leads to the acceptance of a specific proposition. Such systematic studies require the clear formalization of a proposition and the development of a set of premises that may or may not support the conclusion. Logic as systematic study allows advanced engineering informatics researchers to formalize rules of inference that engineers use to arrive at conclusions, make decisions, or creatively develop design ideas.

In particular, the last point—developing creative design ideas—requires a thorough attention to logic. Currently, more often than not, the formalization of rules of inference can lead to logic that are to rigid or that focus on the formalization of irrelevant inference rules. In these cases, creative engineering design—which is so important for improving complex engineered systems—is inhibited.

However, if applied well, logic allows the development of a theory of the intelligent reasoning approaches that engineers follow. Logic allows the formalization of complex engineering understanding about an engineering systems’ behavior across space and time with respect to specific changes of the system under various specific environmental influences. Logic also allows the formalization of knowledge about important procedures that are required during production, or while maintaining an engineering system. Equally important to the formulation of knowledge about processes and procedures is that logic helps engineers account for specific constraints that bound such processes and procedures.

Both ontology and knowledge allow engineers to analyze complex engineering knowledge about the structure of an engineering system and its behavior, as well as procedures for its production and maintenance. However, ontology and logic by themselves do not yet allow for the description of engineering purpose. A classical example of this shortcoming is provided by Sowa[8], drawing upon Newton’s second law of motion that relates force, mass, and acceleration. Newton’s equation introduces an ontology that provides a clear and abstract description of the aspects related to the motion of an object. The formula also represents the logic of how force, mass, and acceleration are related. However, the formula itself does not yet propose how an engineer can use it to purposefully analyze a system. An engineer can use the law for three major purposes: to calculate mass from force and acceleration, force from mass and acceleration, or acceleration from mass and force. Which of these purposes is important for an engineer for a specific engineering task can only be formulated by representing the computation that is required within the specific context the engineer is in. Hence, purpose needs to be explicitly formulated while representing and formalizing engineering knowledge.

Next to purpose, thought needs to specifically be applied towards the concept of "context" while formalizing engineering purpose. To a certain extent, it is impossible to define purpose without such attention to context. At the same time, however, it is important to consider context with respect to the knowledge formalized with ontology and logic. Both ontology and logic are models, and hence it is important to be explicit when and in which circumstances these models are applicable and when these might fail. Hence, understanding context is another important research activity within the field of advanced engineering informatics.

It is important for advanced engineering informatics scholars to consider that ontology, logic, and computation can only represent a very abstract model of the reasoning and knowledge of engineers. Formal knowledge representations are by nature fragmented and cannot get close to the true reasoning engineers use to come to their conclusions for specific tasks. No matter how fragmented and abstract ontologies and reasoning are, they, nevertheless, enable efficient communication, not only between engineers but also among advanced engineering informatics scientists.

To illustrate the above points, the following sub-sections describe how four recent studies suggested and validated four different computational methods for formalizing complex engineering knowledge within the area of built environment engineering. The examples have been identified as good practice examples by the two authors based on their experience as editors of the journal Advanced Engineering Informatics. It was not intended within the scope of this paper to provide a structural literature review, but rather to illustrate the above concepts with a number of loosely selected previous research studies.

Example 1: Formalizing engineering knowledge with ontology

The objective of developing formal ontologies is to help humans and computers understand and, hence, fully utilize domain knowledge in various knowledge management systems. Domain ontology schema should be built and updated constantly as a collective intelligence, since knowledge is considered dynamically changing and growing and, most importantly, can be contributed by multiple domain experts.[12]

An example for such a system is Yuan et al.‘s effort to model the residual value risk around the vulnerability of infrastructure projects.[13] Financial responsibility on these projects is shared by public and private parties. Understanding financial risks that occur during the delivery life-cycle of such projects is important. Estimating these risks is a complex task that engineers are concerned with already during the conceptual design stages, and that is crucial to thoroughly draft contractual agreements between the public and private partners involved in such projects.

Yuan et al. formalized the engineering knowledge of this specific domain by proposing an ontology represeting risk sources, risk events, risk consequences, exposures, resilience factors, and contextual sensitivity characteristics that might influence the risks of a specific project. The study also instantiated the ontology formalizing the specific knowledge of an illustrative bridge project and validated the ontology by conducting a survey among domain experts.

The study shows the utility of formalizing knowledge using ontologies. The authors illustrate how the ontology allows to visualize the risk factors using knowledge graphs and how these visualizations helped to estimate the financial risks of a project. The study also illustrates how the formal representation of the knowledge allows the computation of automated reasoning paths, for example, to understand the effect of design or environmental changes on a specific risk profile.

Example 2: Using logic to represent design knowledge

An example of how to use logic to formalize engineering knowledge can be found in Min et al.‘s study that developed rule-based patterns for laying out theme parks.[14] Designing leisure spaces in a theme park is a highly knowledge-intensive activity. Theme parks need to provide a highly complex and multi-layered service environment to satisfy visitors. In their study, Min et al. identified and formalized patterns used in a number of successful theme parks and combined them into a reasoning system.

Some logical patterns formalized in the study are, for example, that facilities such as attractions, restaurants, and shops are equally distributed around a park’s centroid. Another logical pattern Min et al. identified and formalized is that building entrances are located at pathways that exhibit relatively low traffic. The authors also illustrated how these patterns can be used by developing a software implementation for theme park design and applying the software to design a new theme park in South Korea. The logic was validated by interviewing experts and by conducting design experiments with four experienced experts.

Example 3: Optimization

Much work within the field of advanced engineering informatics has focused on how design optimization can support engineers to identify optimal designs among a set of alternatives. During design optimization, ontology and logic play an important role, as it is required to devise a mathematical formulation of the design problem. To develop this formulation, researchers have to identify variables that describe the alternatives and then relate these variables logically within an objective function that is to be maximized or minimized. Additionally, a number of constraints have to be logically formulated based on the initial design variables. If design problems can be formulated adequately, a large number of computational optimization methods are available that can be applied. While the development of new optimization algorithms would rather fall within the domain of computer science or mathematics, the formulation of design optimization problems is an important topic of advanced engineering informatics research.

An example of research that formulates a design optimization problem around a complex engineering task is Jin et al.‘s study into how to formulate the planning of scaffolding required for complex piping installation work.[15] Designing the best set-up for scaffolding is a complex engineering task because of the spatial relationship between the locations in which work needs to be supported and the requirements to set-up supporting structures.

For formulating the optimization problem, Jin et al. developed a rule-based logic of scaffolding placement and linked these rules with an explicit ontological description combining the timing of construction work tasks, the location of these tasks, and of the pipes’ geometries. The formulation also included different possible postures that workers can be in to install a pipe in an attempt to allow for the optimization of ideal working postures for productive installation work. Different constraints were also formulated such as minimum and maximum acceptable heights for working conditions to bound the automated alternative generation.

The formulation of the optimization problem was validated on a practical example concerned with the installation of 71 different pipes for a 21-meter-high industrial plant. This illustrative validation could vividly show how a knowledge representation can help site engineers to set up optimal scaffolding that minimizes the amount of pipes that cannot be installed with a specific set-up while maximizing the productivity of installation work.

Example 4: Advanced data analytics

Similar to optimization, the last two decades have seen a large amount of studies that applied advanced data analytics methods, in particular neural network-based machine learning, in an effort to develop methods for supporting engineers. Similar to optimization studies, it is important to focus explicitly on the representation of complex engineering knowledge that is inherent to data analytics studies in order to make those studies meaningful to engineering practice. From a knowledge representation perspective, machine learning methods transform collected data input that engineers can not easily interpret to an output that is interpretable to engineers.[8] Advanced engineering informatics research studies these aspects of knowledge representation around the application of well-known data analytics methods from computer science and mathematics. Advanced engineering informatics also explores how such knowledge representations and translations can help engineers to deal with their complex engineering tasks.

An example of such a study is the work by Leng et al.[16] that developed a forecasting method for wind power systems. In their work, the authors suggest a method to translate wind power signals that are hard to interpret by applying the ridgelet transform method, which allows to mathematically model the singular changes within the wind signal more accurately. Using the wind signals as input features, with the ridgelet transform adjusted, a neural network can be trained to forecast wind power accurately.

The authors then demonstrate the utility of the suggested forecasting method by applying it to a wind farm in Alberta. The authors were able to illustrate how the method allows interpretable outputs forecasting wind power for different yearly seasons and even specific days. These predictions can then be used by wind farm engineers to not only design better wind farm layouts, but also to improve the maintenance and management of wind farms in operation.

Research methods

After providing a philosophical basis for research within the field of advanced engineering informatics through focusing the discussion on ontology, logic, purpose, and context, this section will provide some fundamental propositions for how to methodologically approach scientific research. To date, there has been little discussion about research methods in the field, and clear conventions for how to systematically approach research questions have been largely missing. Scientific methods should be empirical in their approach to acquiring knowledge, and they should allow researchers to apply skepticism about what is empirically observed. With this in mind, the section is structured around the two major research tasks of first developing knowledge representations and secondly how to verify, validate, and illustrate them.

Developing formalizations

One of the major research tasks of advanced engineering informatics is the formalization of knowledge with ontology and logic and to represent it. For formalization, well-established methods exist and are widely used. For ontologies, researchers often generate knowledge maps that represent the different identified concepts and that can be created with ontological editors, such as the widely used Protégé.[17] Logic is usually represented in mathematical notations, algorithms in pseudo code, or process diagrams. Metamodels, such as the ones listed above, for representing formalizing models of engineering knowledge are in widespread use within the research community.

However, little thought has so far been given towards how to systematically elicit complex engineering knowledge from experts and engineering practice. By large, most of the knowledge formalized represents the knowledge of the researchers themselves. Pragmatically, this approach has helped the field to develop because the researchers are often also expert engineers. Scientifically, however, this practice is challenging as it lacks the empirical and systematic character that is asked for by sound scientific practice. Only a few specific methods have been proposed or utilized to elicit engineering knowledge from practitioners.[18][19][20] What is common among the few suggested methods is that they either rely upon social science-based methods or on the formal analysis of engineering documents and models.

Social science-based methods that have been suggested in the past were based on interviews with engineers and the organization of workshops and focus groups, as well as more experimental methods. One of the more advanced methods proposed is the use of so-called design charrettes. (Senescu and Haymaker, 2013) Design charrettes propose to capture knowledge of engineers by organizing short, intensive design exercises with professionals that can be observed. An analytical analysis of these observations then allow researchers to better understand the knowledge the engineers applied during the charrette for the consecutive structural formalization. As another example of a social science approach, Hartmann et al. proposed the ethnographic action research method.[18] Ethnographic action research suggests that the researcher gets immersed in engineering work settings by working alongside practicing engineers. They suggest that this immersion allows the researcher to gain the necessary deep insights about practical engineering knowledge prior to formalization.[18]

Next to social science research methods, researchers have relied on the structured analysis of documents and other artifacts. As discussed earlier, Min et al.[14], for example, have formalized design patterns for the design of theme parks. The study identified the patterns through a structured analysis of design documents existing for different realized theme parks.

With the advancement of text mining and pattern recognition techniques, recently many studies have been published that use automated methods to extract information from existing design documents and models. This field has developed into a vibrant research area. Automated text mining methods have been applied to formalize engineering knowledge from patents[21][22], identify research trends for building energy savings[23], analyze construction site accidents[24], predict construction cost overruns[25], retrieve CAD drawings[26], or extract best practices from simulation modeling guidelines.[27] Additionally, several graph-based pattern mining methods have been applied to architectural floor plan design[28] or to the automated extraction and formalization of construction process patterns.[29] Finally, geometric pattern recognition techniques have also been developed to support the aerodynamic design of vehicles.[30]


References

  1. ↑ Mitropoulos, P.; Tatum, C.B. (2000). "Forces Driving Adoption of New Information Technologies". Journal of Construction Engineering and Management 126 (5): 340–8. doi:10.1061/(ASCE)0733-9364(2000)126:5(340). 
  2. ↑ Linderoth, H.C.J. (2010). "Understanding adoption and use of BIM as the creation of actor networks". Automation in Construction 19 (1): 66–72. doi:10.1016/j.autcon.2009.09.003. 
  3. ↑ Davis, K.A.; Songer, A.D. (2009). "Resistance to IT Change in the AEC Industry: Are the Stereotypes True?". Journal of Construction Engineering and Management 135 (12): 1324-1333. doi:10.1061/(ASCE)CO.1943-7862.0000108. 
  4. ↑ de Weck, O.L.; Roos, D.; Magee, C.L. (2011). Engineering Systems: Meeting Human Needs in a Complex Technological World. MIT Press. ISBN 9780262016704. 
  5. ↑ Hartmann, T.; van Meerveld, H.; Vossebeld, N. et al. (2012). "Aligning building information model tools and construction management methods". Automation in Construction 22 (1): 605–13. doi:10.1016/j.autcon.2011.12.011. 
  6. ↑ Hartmann, T. (2011). "Goal and Process Alignment during the Implementation of Decision Support Systems by Project Teams". Journal of Construction Engineering and Management 137 (12): 1134–41. doi:10.1061/(ASCE)CO.1943-7862.0000389. 
  7. ↑ 7.0 7.1 Kunz, J.C.; Smith, I.F.C.; Tomiyama, T. (2002). "Editorial". Advanced Engineering Informatics 16 (1): 1–2. doi:10.1016/S1474-0346(02)00004-6. 
  8. ↑ 8.0 8.1 8.2 8.3 8.4 Sowa, J.F. (2014). Principles of Semantic Networks: Explorations in the Representation of Knowledge. Elsevier. ISBN 9781483221144. 
  9. ↑ Kotis, K.; Papasalourou, A.; Vouros, G. et al. (2011). "Enhancing the Collective Knowledge for the Engineering of Ontologies in Open and Socially Constructed Learning Spaces". Journal of Universal Computer Science 17 (12): 1710–42. doi:10.3217/jucs-017-12-1710. 
  10. ↑ Huang, C.J.; Trappey, A.J.C.; Wu, C.Y. (2008). "Develop a Formal Ontology Engineering Methodology for Technical Knowledge Definition in R&D Knowledge Management". In Curran, R.; Chou, S.Y.; Trappey, A.. Collaborative Product and Service Life Cycle Management for a Sustainable World. Springer. pp. 495–502. doi:10.1007/978-1-84800-972-1_46. ISBN 9781848009721. 
  11. ↑ Hartmann, T.; Amor, R.; East, E.W. (2017). "Information Model Purposes in Building and Facility Design". Journal of Computing in Civil Engineering 31 (6): 04017054. doi:10.1061/(ASCE)CP.1943-5487.0000706. 
  12. ↑ 12.0 12.1 Valarakos, A.G.; Karkaletsis, V.; Alexopoulou, D. et al. (2006). "Building an allergens ontology and maintaining it using machine learning techniques". Computers in Biology and Medicine 36 (10): 1155-1184. doi:10.1016/j.compbiomed.2005.09.007. 
  13. ↑ Yuan, J.; Li, X.; Chen, K. et al. (2018). "Modelling residual value risk through ontology to address vulnerability of PPP project system". Advanced Engineering Informatics 38: 776-793. doi:10.1016/j.aei.2018.10.009. 
  14. ↑ 14.0 14.1 Min, D.A.; Hyun, K.H.; Kim, S.-J. et al. (2017). "A rule-based servicescape design support system from the design patterns of theme parks". Advanced Engineering Informatics 32: 77–91. doi:10.1016/j.aei.2017.01.005. 
  15. ↑ Jin, H.; Nahangi, M.; Goodrum, P.M. et al. (2017). "Model-based space planning for temporary structures using simulation-based multi-objective programming". Advanced Engineering Informatics 33: 164–80. doi:10.1016/j.aei.2017.07.001. 
  16. ↑ Leng, H.; Li, X.; Zhu, J. et al. (2018). "A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting". Advanced Engineering Informatics 36: 20–30. doi:10.1016/j.aei.2018.02.006. 
  17. ↑ Gennari, J.H.; Musen, M.A.; Fergerson, R.W. et al. (2003). "The evolution of Protégé: An environment for knowledge-based systems development". International Journal of Human-Computer Studies 58 (1): 89–123. doi:10.1016/S1071-5819(02)00127-1. 
  18. ↑ 18.0 18.1 18.2 Hartmann, T.; Fischer, M.; Haymaker, J. (2009). "Implementing information systems with project teams using ethnographic–action research". Advanced Engineering Informatics 23 (1): 57–67. doi:10.1016/j.aei.2008.06.006. 
  19. ↑ Senescu, R.R.; Haymaker, J.R. (2013). "Evaluating and improving the effectiveness and efficiency of design process communication". Advanced Engineering Informatics 27 (2): 299–313. doi:10.1016/j.aei.2013.01.003. 
  20. ↑ Jung, N.; Lee, G. (2019). "Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning". Advanced Engineering Informatics 41: 100917. doi:10.1016/j.aei.2019.04.007. 
  21. ↑ Govindarajan, U.H.; Trappey, A.J.C.; Trappey, C.V. (2019). "Intelligent collaborative patent mining using excessive topic generation". Advanced Engineering Informatics 42: 100955. doi:10.1016/j.aei.2019.100955. 
  22. ↑ Wang, J.; Chen, Y.-J. (2019). "A novelty detection patent mining approach for analyzing technological opportunities". Advanced Engineering Informatics 42: 100941. doi:10.1016/j.aei.2019.100941. 
  23. ↑ Ding, Z.; Li, Z.; Fan, C. (2018). "Building energy savings: Analysis of research trends based on text mining". Automation in Construction 96: 398–410. doi:10.1016/j.autcon.2018.10.008. 
  24. ↑ Zhang, F.; Fleyeh, H.; Wang, X. et al. (2019). "Construction site accident analysis using text mining and natural language processing techniques". Automation in Construction 99: 238–48. doi:10.1016/j.autcon.2018.12.016. 
  25. ↑ Williams, T.P.; Gong, J. (2014). "Predicting construction cost overruns using text mining, numerical data and ensemble classifiers". Automation in Construction 43: 23–29. doi:10.1016/j.autcon.2014.02.014. 
  26. ↑ Yu, W.-D.; Hsu, J.-Y. (2013). "Content-based text mining technique for retrieval of CAD documents". Automation in Construction 31: 65–74. doi:10.1016/j.autcon.2012.11.037. 
  27. ↑ Kestel, P.; Kügler, P.; Zirngibl, C. et al. (2019). "Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes". Advanced Engineering Informatics 39: 292–305. doi:10.1016/j.aei.2019.02.001. 
  28. ↑ Strug, B.; Ślusarczyk, G. (2009). "Reasoning about designs through frequent patterns mining". Advanced Engineering Informatics 23 (4): 361–69. doi:10.1016/j.aei.2009.06.007. 
  29. ↑ Sigalov, K.; König, M. (2017). "Recognition of process patterns for BIM-based construction schedules". Advanced Engineering Informatics 33: 456–72. doi:10.1016/j.aei.2016.12.003. 
  30. ↑ Graening, L.; Sendhoff, B. (2014). "Shape mining: A holistic data mining approach for engineering design". Advanced Engineering Informatics 28 (2): 166–85. doi:10.1016/j.aei.2014.03.002. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.

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