By admin
Series: The Automations I Used…or Wished I Used
Introduction
In the rapidly evolving field of civil engineering, automation has become an essential driver of efficiency, accuracy, and innovation. Prior to starting my AI-focused company, I worked as an engineering consultant, primarily structural, at four different companies. Throughout my career, I’ve experienced firsthand the transformative impact automation tools can have on daily tasks and complex project workflows. This newsletter series aims to highlight various automation strategies; both those I successfully employed and those I wish I’d known sooner. It is meant to help you streamline your engineering processes and focus more on the creative and analytical aspects of your work.
This series will explore various cutting-edge automation tools and methodologies, including Python scripting, Natural Language Processing (NLP), AI-driven agents, and specialized AI providers. This first newsletter provides a comprehensive introduction to these topics, setting the stage for a deeper examination in upcoming issues.
1. Python Automation for Structural Engineering
Python has emerged as a go-to programming language for engineers due to its readability, ease of use, and powerful libraries. Conveniently, it is also the coding language of machine learning. Integrating Python scripting into structural engineering software like SAP2000, ETABS, and RISA enhances productivity and accuracy.
Automating Structural Analysis Software: Python scripts interact directly with this software through Application Programming Interfaces (APIs), automating routine tasks such as model creation, applying load conditions, and extracting analysis results. Scripting allows engineers to automate data entry, reducing manual effort and minimizing errors.
Automated Report Generation: Formulaic reports can be written through Python scripts to significantly improve workflow efficiency. Engineers frequently spend considerable time manually compiling and formatting reports. Having a code that facilitates the automatic filling of predefined templates, ensures uniformity and saves considerable time.
I personally had a client whose weekly reports required extensive repetitive input. Implementing Python-driven automation transformed a previously time-consuming task into a seamless, efficient process, turning hours of manual labor into a short routine task.
“From the PSR report generator to the SID Code system, they delivered custom, practical solutions that save us time and streamline our workflows, including our peer review process, which is critical to how we work.”
2. Natural Language Processing (NLP) for Excel and Task Optimization
Excel spreadsheets remain indispensable in engineering offices but can become burdensome when manually managing large datasets. NLP technology offers a solution to automate Excel tasks and streamline data extraction from documents.
Excel meets NLP: Tools like ChatGPT, Claude, Grok, and Gemini significantly simplify Excel-related tasks by generating formulas based on natural language prompts and recommending a variety of starting points based on a desired result.
Moreover, NLP technologies allow engineers to extract essential data from unstructured manuals and specifications swiftly. Gathering data from tables has never been easier. Engineers can also provide textual prompts to NLP tools and quickly generate structured spreadsheets, eliminating tedious data entry tasks and minimizing errors.
Task Planning and Optimization: NLP can also assist in strategizing task execution by analyzing project documentation and recommending optimal workflows. By interpreting and structuring vast amounts of textual data, NLP tools can suggest practical approaches, enhancing decision-making processes within projects. Sidian has created a great tool to narrow the way the LLMs “think” based on documents you upload. The basic procedure is you upload a document, or multiple, ask a question based on that document information, and get a response. References to which page as well as a PDF viewer are also provided so that answer verification is seamless. We are excited to release it in the coming weeks.
3. AI Agents in Civil Engineering
AI agents represent a significant leap forward in engineering automation, performing tasks autonomously and improving productivity. These intelligent agents can learn, adapt, and independently execute tasks, substantially streamlining engineering workflows.
Understanding AI Agents: AI agents are software programs designed to carry out specific tasks autonomously, learn from data inputs, and interact intelligently with their environment. The agent is composed of five parts. The LLM which is the brain, the prompt which is the instructions, the memory which typically comes along with the brain and LLM besides a few unique cases, the tools which are like functions that take some type of input and convert it into a desired output, and then external knowledge which can be used to customize the brain or bring it up to date on current topics (typically LLMs are a year behind due to the data they have been trained on).
AEC Magazine highlights the efficacy of AI agents in civil engineering, particularly in project management and workflow optimization. These agents can compile necessary documentation, provide regular project updates, and monitor relevant industry news, freeing engineers to focus more on critical design and strategic decisions.
Practical Applications: AI agents are highly versatile and can operate individually or as part of a coordinated digital workforce. For example, employing a team of AI agents to monitor various aspects of a construction project can significantly enhance project management efficiency and information accuracy.
My personal experience with AI agents involved deploying solutions that delivered daily summaries of essential project news, updating me on key aspects of my work and personal life, and preparing for presentations and meetings.
According to Naval Ravikant, a prominent entrepreneur and the Founder of AngelList, “The next billion-dollar company will be built by two people”
4. Existing AI Solution Providers in Civil Engineering
Civil engineering firms seeking AI-driven solutions typically encounter two categories of service providers: subscription-based platforms and customized solution developers. Both categories offer distinct advantages depending on specific project needs and firm preferences.
Subscription-Based Providers:
- VIKTOR: Provides an intuitive, Python-based platform allowing engineers to create custom web applications that integrate seamlessly with existing engineering software in a friendly user-interface (VIKTOR).
- Civils.ai: Specializes in extracting and analyzing data through AI-driven tools, simplifying and expediting engineering workflows (Civils.ai).
- Togal.AI: Employs sophisticated algorithms to automate quantity takeoffs from construction plans, drastically enhancing the speed and accuracy of project estimations (Togal.AI).
Customized Solution Providers:
- Workorb: Offers tailored automation for proposal generation and business development tasks, greatly increasing operational efficiency in architecture, engineering, and construction firms (Workorb).
- Joist.ai: Automates proposal creation and marketing materials, enabling rapid, personalized content generation tailored explicitly to client specifications (Joist.AI).
Choosing between subscription-based and customized solutions depends on the specific automation needs of your engineering firm, with subscription-based platforms offering rapid implementation and scalability, while customized providers offer specialized solutions tailored closely to unique business requirements.
More on these various providers can be found on the Sidian Website.
Conclusion
Automation technologies are revolutionizing civil engineering by reducing manual workload, increasing precision, and improving overall productivity. Integrating Python scripting, NLP technologies, AI agents, and specialized AI solutions into your workflows can significantly enhance efficiency and project outcomes.
In future newsletters, we will go deeper into each automation area, providing specific implementation examples, case studies, and practical guidance to support your transition towards a more automated, efficient, and error-free engineering practice. Stay tuned as we explore these exciting developments in detail, equipping you with the knowledge and tools to propel your engineering processes forward.