In the previous installment of this series, we explored the leadership perspectives and managerial considerations surrounding computational thinking (CT) and its application in the development of computational design tools. Through the lens of the Office Space Automator (OSA) project, we highlighted the role leaders play in facilitating and guiding projects driven by CT principles.

This article shifts our focus to computational engineers’ practical application of CT during the logic derivation phase of computational tool development. Continuing to use the OSA project as a case study, we will delve into computational engineers’ specific responsibilities and workflows as they translate domain knowledge into executable computational logic.

Logic derivation is a core component of any computational engineering endeavor. It involves extracting and formalizing domain-specific knowledge, design principles, and problem-solving heuristics into a structured computational representation. This computational logic is the foundation upon which computational tools are built, enabling the automation and optimization of complex tasks and processes.

In the context of the architecture, engineering, and construction (AEC) industry, effective logic derivation is crucial for capturing the collective expertise and best practices of various engineering disciplines. By translating this knowledge into computational form, computational engineers enable the creation of powerful tools that can streamline workflows, perform optimizations, and enhance design accuracy.

Moreover, the logic derivation phase builds a bridge between technical experts and computational developers. It serves as the interface where domain-specific knowledge is abstracted and formalized, allowing computational developers to subsequently transform this logic into functional software tools.

As the demand for computational design tools grows within the AEC industry, the importance of logic derivation cannot be overstated. Computational engineers skilled at this task are essential for developing computational tools that accurately reflect industry best practices, adhere to relevant codes and standards, and ultimately deliver value to end-users, clients, and stakeholders.

1. Case Study Overview

The OSA project was an initiative undertaken by Henderson Engineers’ Innovation Department. As a reminder, its primary objectives were twofold: to formalize the firm’s collective business logic and expertise across various engineering disciplines and to develop a user-friendly computational tool for automating the office space design process in Revit.

These objectives were closely aligned with Henderson’s core values and strategic goals, which emphasize innovation, efficiency, and delivering exceptional value to clients. The OSA project aimed to streamline workflows, reduce errors, and enhance the overall quality and consistency of office space designs by encapsulating the firm’s domain knowledge into a computational framework.

The OSA project followed a structured approach, divided into two distinct phases: logic derivation and tool development. In the logic derivation phase, computational engineers worked closely with technical experts from the relevant engineering disciplines to translate their domain knowledge into a formal computational representation. This process involved abstracting and deconstructing complex design principles, regulatory requirements, and industry standards, allowing them to be expressed in a form that could be interpreted and executed by a computational system.

While the broader OSA project encompassed both the logic derivation and tool development phases, this article will primarily focus on the logic derivation portion, which fell under the purview of computational engineers. By examining the logic derivation process in detail, we aim to provide valuable insights and best practices for computational engineers working on similar projects, where the accurate translation of domain expertise into computational logic is critical for the successful development of innovative computational tools.

1. Role of Computational Engineers

Computational engineers played a key role in the OSA project, serving as the bridge between the technical experts and the computational developers tasked with developing the tool. Their primary responsibility was to acquire and formalize the knowledge and expertise of technical experts into a structured, computational representation that could be translated into software code.

Throughout the logic derivation phase, computational engineers were expected to collaborate closely with technical experts to facilitate an effective knowledge transfer. They were responsible for breaking down complex design principles, best practices, building code requirements, and problem-solving heuristics into their fundamental components. They were also tasked with the subsequent abstraction and translation of these requirements into unambiguous computational rules.

The logic derived by computational engineers during this phase served as the foundation for computational developers’ subsequent tool development efforts. The accuracy and completeness of the derived logic directly impacted the functionality, reliability, and usability of the resulting computational tool.

Computational engineers facilitated a seamless transition between the logic derivation and tool development phases by thoroughly documenting the computational logic and maintaining clear communication with the tool development team. This collaboration ensured that the computational developers had a comprehensive understanding of the underlying logic, enabling them to translate it into a functional and user-friendly software application.

1. Logic Derivation Workflow 

The logic derivation process is the foundation upon which computational tools are built. In the context of the OSA project, computational engineers followed a well-defined workflow to ensure that various technical experts’ collective knowledge and expertise were accurately captured and translated into a robust computational framework.

3.1. Requirement Gathering and Analysis 

The first step in the logic derivation workflow involved a thorough gathering and analysis of project requirements. This step was an exercise in the first of the key elements of CT: investigation. Computational engineers worked closely with project stakeholders, technical experts, and end-users to understand the specific objectives, constraints, and desired outcomes of the OSA tool.

This process involved conducting extensive interviews, workshops, and other observations to gain a comprehensive understanding of the current office space design practices. Computational engineers meticulously documented these requirements, ensuring they had a solid foundation for building the computational logic.

3.2. Knowledge Acquisition 

With a clear understanding of the project requirements, computational engineers focused on acquiring domain-specific knowledge and expertise from various technical experts. This knowledge acquisition phase served as the primary source of information for real-world design principles and best practices.

Computational engineers employed a range of knowledge acquisition techniques, including structured interviews, over-the-shoulder observations, and collaborative workshops. They fostered an environment of open communication and trust, encouraging technical experts to share the knowledge and decision-making processes that guide their work.

Throughout this phase, computational engineers worked diligently to capture the nuances and contextual factors that influence expert decision-making, recognizing that these elements are often not explicitly documented but are critical for developing accurate and comprehensive computational logic.

3.3. Translating Domain Knowledge 

Armed with a deep understanding of project requirements and a trove of domain-specific knowledge, computational engineers then translated this information into a structured computational representation. While the process of gathering and analyzing requirements used the first element of CT, investigation, this process of translation utilized the remaining elements:

1. **Abstraction**: Computational engineers abstracted away the specific details and complexities of the domain knowledge, identifying the core principles, rules, and relationships that governed the decision-making processes.
2. **Decomposition**: They broke down these core principles and rules into their fundamental components, creating a hierarchical structure that facilitated a more granular understanding and the subsequent translation into computational logic.
3. **Pattern Recognition**: Computational engineers analyzed the acquired knowledge to identify recurring patterns, heuristics, and decision-making frameworks that could be generalized and formalized into computational algorithms.
4. **Logic Design**: The abstracted and decomposed knowledge was formalized into a structured computational framework. This framework served as the blueprint for the subsequent development of the OSA tool, ensuring that the computational logic accurately reflected the collective expertise of the technical experts.

Throughout this translation process, computational engineers collaborated closely with technical experts and stakeholders, regularly validating the derived logic and ensuring that it aligned with industry best practices, regulatory requirements, and the project’s desired outcomes. By following this rigorous logic derivation workflow, computational engineers successfully extracted the domain expertise and translated it in preparation for the tool development phase.

1. Logic Derivation

4.1 Challenges and Solutions 

While essential for the successful development of computational tools, the logic derivation process is not without its challenges. The OSA project faced several hurdles that required innovative problem-solving strategies to overcome. In this section, we will explore some common challenges encountered during the logic derivation phase, along with the solutions and strategies employed by the computational engineers.

1. **Knowledge Acquisition Barriers**: One of the primary challenges was the inherent difficulty in extracting tacit knowledge from technical experts, who often rely on intuition, experience, and “rules of thumb” that are not explicitly documented or easily articulated. To address this, computational engineers adopted an iterative approach to knowledge acquisition, conducting multiple sessions with technical experts and continuously refining and validating the acquired knowledge.
2. **Translating Ambiguity**: Certain aspects of domain knowledge, such as subjective design considerations or context-specific decision-making, can be ambiguous and challenging to translate into precise computational logic. Computational engineers led collaborative sessions where technical experts worked together to articulate and visualize their decision-making processes, fostering a shared understanding.
3. **Balancing Complexity and Abstraction**: Finding the right balance between capturing the necessary details and maintaining an appropriate level of abstraction was a constant challenge during the logic derivation process. To navigate this challenge, they developed prototypes and conducted regular validation sessions with technical experts to ensure the derived computational logic accurately represented their expertise.
4. **Ensuring Consistency and Accuracy**: With technical experts from multiple engineering disciplines contributing their knowledge, it was important to ensure consistency and accuracy in the derived computational logic. Meticulous documentation and knowledge management systems were instrumental in maintaining consistency—their use standardized knowledge sharing and enabled traceability of the derived logic.
5. **Stakeholder Alignment**: Aligning the derived logic with the diverse perspectives and expectations of various stakeholders, including end-users, subject matter experts, and sector leadership, posed a recurring challenge. Computational engineers actively engaged with stakeholders throughout the logic derivation process, seeking feedback, addressing concerns, and ensuring alignment with the project’s objectives.

4.2. Lessons for Future Endeavors 

Through the challenges and solutions encountered during the OSA project, several best practices and lessons learned emerged:

1. **Foster Open Communication and Trust**: Establishing an environment of open communication and trust between computational engineers and technical experts encourages effective knowledge transfer.
2. **Embrace Iterative Processes**: Logic derivation is an iterative process that requires continuous refinement and validation. Embracing this iterative nature is essential for achieving accurate and robust computational logic.
3. **Leverage Visualization Techniques**: Visual representations and modelling techniques can significantly aid in articulating and understanding complex domain knowledge, bridging the gap between technical experts and computational engineers.
4. **Prioritize Documentation**: Thorough documentation ensures consistency and traceability and improves knowledge sharing and collaboration within the team and across future projects.
5. **Continuous Learning and Adaptation**: Computational engineers should remain open to continuous learning and adapt as necessary, as new challenges and domain-specific nuances may arise throughout the logic derivation process.

By recognizing and proactively addressing these challenges, the OSA project’s computational engineers could derive accurate and robust computational logic that effectively captured the collective expertise of the technical experts.

1. Broader Implications

While the primary objective of the logic derivation phase is to develop the computational framework for a specific tool or application, its implications extend far beyond the immediate project scope. Effective logic derivation lays the foundation for more complex automations and optimizations.

5.1. Enabling Innovation and Problem-Solving Efficiency 

By accurately capturing and formalizing domain knowledge and engineering design principles, computational engineers unlock the potential for new problem-solving approaches. The derived computational logic serves as a foundation upon which new solutions can be rapidly prototyped, tested, and refined, accelerating the pace of innovation.

Furthermore, organizations can significantly enhance their problem-solving efficiency by encapsulating expert knowledge and best practices into executable computational frameworks. Computational tools can automate complex tasks, optimize processes, and provide consistent and reliable solutions, freeing up valuable human resources to focus on more challenging issues and higher-level strategic initiatives.

5.2. Abstracting Engineering Design Principles 

The logic derivation process provides a systematic approach to abstracting and formalizing engineering design principles across various disciplines. Computational engineers can uncover common principles and heuristics that transcend specific disciplines by breaking down complex domain knowledge into its fundamental components and identifying underlying patterns and relationships.

This cross-disciplinary abstraction fosters a deeper understanding of engineering principles and paves the way for the development of interdisciplinary computational tools. These tools can seamlessly integrate knowledge from multiple domains, enabling holistic solutions that address complex, multifaceted problems.

5.3. Knowledge Management 

Effective logic derivation inherently involves collaboration between computational engineers and technical experts from various disciplines. This collaborative process promotes knowledge sharing, cross-functional understanding, and the development of a shared vocabulary and conceptual framework.

The logic derivation phase facilitates the transfer of expertise between individuals and across teams by providing a structured and systematic approach to capturing and representing domain knowledge. This knowledge transfer not only enhances the collective understanding within an organization but also ensures the preservation and dissemination of valuable expertise, mitigating the risks associated with employee turnover or knowledge silos.

Moreover, the derived computational logic and the accompanying documentation serve as a valuable knowledge repository, enabling future teams to build upon existing knowledge and accelerate the development of new computational tools or refine existing ones.

1. Conclusion

Throughout this exploration of the OSA project, we have witnessed computational engineers’ important role in the logic derivation phase of computational tool development. Acting as bridges between technical experts and computational developers, these engineers are tasked with the critical responsibility of translating domain knowledge into robust computational logic.

Their structured workflow encompasses requirement gathering, knowledge acquisition from technical experts, and formalizing this acquired knowledge into an executable computational framework. To accomplish this, computational engineers leverage a diverse array of techniques, including knowledge representation and modelling approaches, as well as CT principles such as abstraction, decomposition, and pattern recognition. Specialized software solutions further aid in streamlining and enhancing this intricate process.

However, the path to effective logic derivation is not without its challenges. Computational engineers must navigate knowledge acquisition barriers, translate ambiguous domain knowledge, balance complexity and abstraction, ensure consistency across multiple sources, and align diverse stakeholder perspectives. Overcoming these hurdles requires innovative problem-solving strategies, iterative processes, and adopting best practices gleaned from real-world projects like the OSA.

Ultimately, the implications of logic derivation extend far beyond the immediate project scope. By accurately capturing and formalizing domain knowledge, computational engineers enable innovation, enhance problem-solving efficiency, and facilitate cross-disciplinary collaboration and knowledge transfer within their organizations. The derived computational logic is a foundation for rapid prototyping, process optimization, and the development of holistic, interdisciplinary solutions to complex, multifaceted problems.

The demand for computational tools is experiencing a significant surge, and as a result, the role of computational engineers has become increasingly crucial in unlocking the complete potential of CT and propelling innovation forward. To meet the increasing demand, computational engineers must pay attention and respond to a strong call for action.

First and foremost, they must continuously develop and refine their knowledge acquisition skills, fostering open communication and trust with technical experts to extract and capture their expertise effectively. Staying up to date with the latest knowledge representation and modelling techniques, as well as CT approaches, is essential to ensure their ability to translate complex domain knowledge into accurate and efficient computational logic.

Furthermore, computational engineers must embrace collaboration and actively engage with stakeholders throughout the logic derivation process, ensuring alignment with project objectives and facilitating knowledge sharing across disciplines. Cultivating a mindset of continuous learning and adaptation is paramount, as logic derivation often involves navigating ambiguity and addressing unforeseen challenges.

Perhaps most importantly, computational engineers must recognize the broader implications of their work and strive to contribute to the advancement of innovation, problem-solving efficiency, and cross-functional collaboration within their organizations. By heeding this call to action, they can position themselves as indispensable assets, driving the development of cutting-edge computational tools that revolutionize industries and unlock new frontiers of problem-solving capabilities.

More from Henderson on Computational Thinking:

• Part 1: Computational Thinking in the AEC Industry
• Part 2: Exploring Computational Thinking in the AEC Industry
• Part 3: Deciphering the Principles of Computational Thinking
• Part 4:
  • Exploring the Key Elements of Computational Thinking (Part 1)
  • Exploring the Key Elements of Computational Thinking (Part 2)
• Part 5: Leadership Perspectives on Computational Tool Development