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Explore a wealth of engineering articles & resources specially designed for students. Stay informed with the latest trends & insights. Read now

Engineering Articles & Resources - CalcTree

Transform Your Engineering Spreadsheets into Web Apps with CalcTree: A Step-by-Step Guide 

The Rise of Generative AI And Its Challenges

ChatGPT Challenges in Engineering Data Calculation 

Construction industry challenges: Common Data Environments

Importance of data integration for the construction industry

Why Cloud-Based Software is the Future of the Construction Industry

Civil Engineering Software To Transform Your Calculations

Transform Your Python Script into a Web App with CalcTree: A Step-by-Step Guide

Streamlining Your Workflow: How to Connect Spreadsheets and Python Scripts with CalcTree

Embedding Spreadsheet Values in Reports with CalcTree

Enhancing Civil Engineering Calculations in Chatbots with CalcTree API Integration

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Engineering Technology Article - CalcTree 

Generative AI is quickly becoming a transformative force in construction and engineering design. By leveraging existing engineering knowledge and judgement capital, it has the potential to revolutionize how we design everything from skyscrapers to bridges.

Firms' ability to capture and utilize historical data and information within their human capital will differentiate their capacity to enhance their projects and workforce with generative AI. So, firms must understand better and address how they capture and collate tactic knowledge.

This article explores the impact of generative AI on construction design and the pivotal role of CalcTree, a platform that allows designers to harness years of technical expertise, design principles, and empirical knowledge.

The Untapped Wealth of Tacit Engineering Knowledge

When we speak of existing engineering knowledge, we often fail to distinguish between explicit knowledge, readily available in documents, reports, and databases, and tacit knowledge, also known as 'engineering judgement'. This tacit knowledge, expertise and know-how built up over years of practice, are stored in the minds of experienced engineers. It forms the primary competitive edge for consulting and design firms in particular, that's why these firms so fiercely compete for talent. But due to its nature, such information is hard to access and challenging to structure in a way that a machine can interpret. However, doing so is key to unlocking genuinely transformative design possibilities with data-driven AIs.

One of the most critical prerequisites for 

 is capturing and organising tacit knowledge alongside empirical data and explicit information. AI can only learn from and generate new designs using the data it's been trained on. Therefore, unrecorded expertise or undocumented design cannot be leveraged by an AI that needs that information to generate new ideas. So capturing, organizing, and making tacit knowledge explicit is essential in AI-driven engineering design.

calculation and design management platform

, comes in. CalcTree helps bridge the gap between tacit knowledge and the data sets required by AI models. It does this by helping to capture and structure typically unstructured project information, like 

, design principles and notes and boundary conditions, and process knowledge. CalcTree integrates seamlessly with everyday tools such as Excel, Python scripts, reports and simulation models. It enables users to create and collaborate on templates, making the data within these tools accessible and interpretable by systems designed for AI training.

CalcTree is also designed to integrate seamlessly with other tools via API, serving as the perfect backend for engineering and 

 firms exploring generative AI applications. Whether the tacit knowledge resides in a different design tool or database, CalcTree has visibility of it.

Let's take a closer look at the future of engineering design with AI to understand the importance of knowledge capture in more detail. Specifically, how the advent of bespoke AI 'co-pilots' might influence how workers do their jobs, and a company's overall productivity. Let's consider the development and implementation of such co-pilots using Mckinsey's '3 horizons innovation' framework.

Horizon 1: Knowledge Assistants for 'first drafts' and Ideation

Horizon one sees Knowledge Assistants predominantly used for ideation, composition, and text improvement within engineering and construction settings. AI models at this stage are task-specific, and the accessibility and bandwidth of a particular user restrict their interaction with corporate data. These assistants can help with straightforward tasks, such as generating new design ideas or providing first cuts of code and the like. 

However, they still require much human input to be valuable. Additionally, as pure large language models (LLMs) they're very limited in their computational ability, so they have limited value, in particular in the computationally heavy field of engineering. 

As well as this, at horizon one, security and privacy arrangements between 3rd party LLMs and secure and private company operations and projects are not established. As such, many companies are restricting their workforce's usage of tools like chatGPT and Bard.

We can already see the advent of generalised versions of these assistants in the market, Bard, chatGPT etc. But we expect to see more sector-specific versions of these tools, perhaps trained on more specific industry data or with better computational abilities, enter the market over the next 6-18 months. 

Horizon 2: Enhanced Interaction with Private Data

As privacy and security arrangements are established in firms, the second horizon will see the introduction of AI co-pilots with the ability to interact with private data. This means these AI entities will have access to explicit company and project-specific data, giving way to tools that can generate ideas and drafts that consider such information. 

In construction and engineering, at this horizon, we expect to see AI tooling that can reach into CAD, BIM, simulation software and regulatory data. Thereby able to provide insights and user assistance specifically tailored to context.

Some startups are already releasing betas and prototypes of these concepts, and it won't be long until the likes of Microsoft, and Google integrate such AI into their product suites as well. 

The challenge for the construction and engineering design sectors is that project data is highly fragmented and typically spans dozens of data formats across isolated environments.  So reaching horizon 2 in our sector will require broader and agnostic data management tools like CalcTree to reach into and 'flatten' data sets across tooling.

Horizon 3: AI Co-Pilots as Active Contributors

The third horizon would see Knowledge Assistants that can interpret data and then execute tasks on behalf of designers. Through interaction with data management systems and tools, AI co-pilots could start to perform actions such as producing and running simulation models and then applying computational rules and engineering judgement to designs. This would work to overcome the computational limitation of LLMs.

Reaching this stage will require addressing 

 and the diversity of tooling used in design processes. As well as training AI on collated tacit knowledge, as well as explicit contextual information.

Wide-scale deployment may be slower in specific sub-sectors with more design edge cases and niche tooling. So adoption of agnostic data management tools and smart usage of AI interpreters to process, organise and drive niche software will be critical.

Across all these horizons, the key to success and the increasing capability of AI co-pilots lies in the effectiveness of information collection and collation, as well as 'powering-up' general AI (LLMs in this case) with access to third-party tools and data sets. 

This is particularly true for construction and engineering, where data is often spread across different platforms and systems. Platforms like CalcTree play a vital role in this regard, helping to capture and structure both explicit and tacit knowledge that AI can harness across all three horizons, with increasing importance at each level. 

Integrations with critical tried and tested industry software like Revit, Tekla and simulation software - which is also possible in CalcTree, will also pave the way for horizon 2 and 3-level AI assistants.

While the potential benefits are enormous, it's also crucial to consider the challenges and implications of generative AI. Issues around data privacy, algorithm bias, and the need for human oversight must be addressed. Future research and regulatory measures should focus on creating an ethical, inclusive AI framework that serves the needs of all stakeholders.

Additionally, the collection and collation of tacit knowledge, as opposed to just explicit knowledge, is key to unlocking truly powerful AIs which can apply engineering judgement to complex situations. 

Generative AI is poised to bring a paradigm shift to construction and engineering design, unlocking new possibilities and better leveraging our rich engineering knowledge. The future will see AI not as a replacement but as a partner in design, working alongside human engineers to shape the cities and landscapes of tomorrow.

, the app you're reading this one is a calculation management platform. You can sign-up and build hosted, shareable web apps (complete with an API and a web publishing module) with tools like Python and Spreadsheets. Learn more 

Discover the transformative power of Generative AI & its impact on knowledge capture. Unlock insights about the rise of AI technology. Read more!

The Rise of Generative AI And Its Challenges - CalcTree

ChatGPT is a large language model that has been trained on a dataset of conversational text, allowing it to generate human-like responses to text input. It is based on the GPT (Generative Pre-trained Transformer) architecture and was developed by OpenAI. It can be used for various natural languages processing tasks such as language translation, text summarization, and question answering.

At least, that was the response I got when I asked the AI chatbot what ChatGPT is. Think of it as a human encyclopedia. It knows a lot and can converse with you in a very human-like manner. While it has no opinions or feelings, it can still respond to inappropriate behavior, hold sustained conversations, and challenges false information.

ChatGPT was released in November 2022 by OpenAI and has since gained online traction, reaching 100 million users within its first two months of launch. It’s at the center of the discussion of artificial intelligence, being one of the most powerful tools to have come out in recent years. Digital and AI implementation has been on the rise in the construction sector in recent years, and as engineers, we’re excited to check out what

 We will look at how this AI model works and assess its application within an engineering context.

Chat GPT is advanced, very advanced. It doesn’t speak with sentience but rather is driven by AI to craft logically correct responses based on the inputs we, the users, provide. How exactly is the model built?

OpenAI built ChatGPT with the help of Reinforcement Learning from Human Feedback (RLHF). The basic idea behind this method is to allow an AI agent to perform randomly in a given environment with an end goal. Small snippets of their behavioubehavioriewed by a human, who chooses the most desirable action out of the sample. The AI then takes on that feedback and learns to align its actions more and more with the desired actions. You can read more about this method directly from OpenAI 

Chat GPT functions very much like a chatbot, where the user sends a message, and it responds accordingly. It can teach you physics, catch you up on the news and even make a rap about oranges (I would say it turned out quite well)...

While it can perform many functions, language models like 

 One is that it operates on previously provided information instead of updating its knowledge bank with new information as it’s used. It is also a text-based model, so it is impossible to communicate with it visually. Still, it’s incredible technology, so let’s see how useful it is in 

In this section, we will run a few tests on ChatGPT by providing engineering-related prompts to see how well it performs.

Let’s start simple and increase the detail and complexity of information sent to and required from the AI to answer the question correctly and explore what chatgpt challenges arise.

Prompt #1: How do you calculate the reaction forces on a simply supported beam?

The calculation of reaction forces on a simply supported beam involves the use of equilibrium equations and the principles of statics. A simply supported beam is a type of beam that is supported at both ends, allowing rotation but not translation. The reaction forces at each end of the beam must be equal and opposite to the forces acting on the beam in order to equilibrium.

The first step in calculating the reaction forces is to draw a free-body diagram of the beam, showing all of the forces acting on it, including the load and the reaction forces. Next, the equations of equilibrium are used to calculate the reaction forces. The equations of equilibrium state that the sum of the forces in the x-direction and the y-direction must be equal to zero, and the sum of the moments about any point must also be equal to zero.

In the case of a simply supported beam, the reaction forces at each end can be calculated by considering the forces acting on the beam and solving the equations of equilibrium. 

The equations of equilibrium in x-direction and y-direction are:

Where R1 and R2 are the reaction forces at the supports, P is the load acting on the beam, x1 and x2 are the distance from the load to the respective supports, and ΣM is the sum of moments about any point.

Once we have the equations, we can use mathematical methods like substitution or elimination to solve for the unknowns.

Please note that this is a simplified explanation of the process, and in practice, the calculations can be more complex depending on the specific details of the problem such as the shape of the beam, the type of load, and the position of the load.

Ok, not bad at all. The AI lays out the overarching logic for the task and then even presents the correct equations for resolving forces. It even warns that this is a simplified explanation, a bit of a cop-out in our book, but not an unreasonable response by human standards. 

This is high school-level stuff though. So let’s turn it up a notch and ask something a bit more complex.

Prompt #2: Explain how to perform a stiffness analysis on trusses

Stiffness analysis is a method used to determine the behavior of truss structures under loads. The basic principle behind stiffness analysis is that the deformations of a structure are proportional to the applied loads. The stiffness of a structure is a measure of its resistance to deformation, and it's a key factor in 

determining the behavior of a structure under load.

To perform a stiffness analysis on trusses, there are several steps that must be followed:

The first step is to determine the loads that will be applied to the truss. This includes determining the type and magnitude of the loads, as well as their direction and point of application.

The next step is to identify the degrees of freedom of the truss. A degree of freedom is a direction in which a structure can move. For trusses, the degrees of freedom are typically translated in the x, y, and z directions.

3.   Assemble the global stiffness matrix

The global stiffness matrix is a matrix that contains all of the information about the stiffness of the truss. To assemble the global stiffness matrix, the stiffness of each element of the truss must be determined and assembled into a matrix.

The next step is to apply boundary conditions to the global stiffness matrix. A boundary condition is a constraint on the motion of the structure. For trusses, boundary conditions are typically applied at the supports.

Once the global stiffness matrix and boundary conditions have been assembled, the nodal displacements can be calculated using matrix algebra.

The final step is to calculate the member forces using the nodal displacements and the stiffness matrix.

It's worth noting that, in practice, stiffness analysis can become quite complex, especially for large and intricate structures. There are also many different types of stiffness analysis, such as linear or nonlinear, and each one has its own set of steps and calculations. Engineers use specialized software or computer programs to perform stiffness analysis, which can greatly facilitate the process.

So Chat GPT can provide a cohesive summary of 

 methods. Broadly speaking, the above approach is correct, and it matches what you’d find if you punched that question into Google. Unlike in Example 1 however, ChatGPT chooses not to share equations for this process.

Let’s amp it up and try having it help solve specific problems one might see in practice. Right off the bat, we run into a bit of a problem. It’s not a simple task to provide ChatGPT with a structure. Chat GPT is a text-based model. Therefore, we can’t give it anything visual to use. We could translate our structure into points, coordinates, and cod,e, but this is an unrealistic expectation for your average engineer. This limitation is less than ideal in 

civil and structural engineering projects!

Nonetheless, let’s adjust the plan accordingly and send the prompt!

Prompt #3 : UsingAS3600, determine the factored design moment, M* for the simply supported beam under the following conditions

To determine the factored design moment, M*, for the simply supported beam under the conditions described using AS3600, you must consider the load combinations and safety factors specified in the code.

First, you need to calculate the factored load using the dead, live, and point loads. The dead load can be calculated as 6 kN/m * 9 m = 54 kN. The live load can be calculated as 4.5 kN/m * 9 m = 40.5 kN. The total factored load is 54 kN + 40.5 kN = 94.5 kN.

Next, you will need to apply the load combinations specified in the code, which typically include the dead load, live load, and various combinations of these loads, such as 1.2Dead Load + 1.6Live Load. The maximum factored load should be used for the design.

Once you have the factored load, you can calculate the moment of inertia, I, of the beam using the dimensions provided (D = 600 mm, b = 400 mm).

I = (bh^3)/12 = (400600^3)/12 = (400*216000000)/12 = 1440000000 mm^4

Then, using the moment of inertia and factored load you can determine the factored design moment, M* using the following equation:

M* = (factored load * length^2)/8 = (94.5*2.5^2)/8 = 47.125 kN.m

The point load of 75 kN, 2.5 m from the left end will cause an additional moment of 75*2.5 = 187.5 kN.m

Finally, you will need to check if the calculated M* is within the allowable limit provided by AS3600, which is based on the loading type of beam, material properties, and safety factors.

Please note that the above calculation is based on the assumption of the load distribution and the load case scenario. It is important to consult the AS3600 code for the specific load combinations, safety factors, and detailed design requirements.

Very solid attempt, but ChatGPT fell short of a sufficient answer. The chatbot got as far as providing us with the total loading experienced by the beam. Things start falling apart soon after. Let’s point out what went wrong.

So let’s assess the artificial intelligence’s performance in each example.

Why is ChatGPT bad at Calculation, while it is very good at other stuff?

Chat GPT itself is a language model, not a computational one. This is very important as the model approaches the input problems as language problems rather than mathematical ones. When we ask a simple problem such as “10 + 10”, it will return an answer of ‘20’ because, from all the information it’s been trained on, 20 was determined to be the most preferred response based on feedback.

Chat GPT does not perform the calculations itself.

If the program does not run any calculations, no matter how advanced it is, it will always struggle with maths and computation. There is a lot of data in its system to determine the best output for various conversations based on user input and response. 

However, calculations require an understanding of fundamental mathematics and an ability to make judgments based on numerical data. Features that ChatGPT lacks.

However, as shown in the provided prompts, Chat GPT does an impressive job of explaining engineering concepts to us. While it may be unable to make judgments in calculating the solution to new problems, it has been trained with numerous explanations and resources on concepts in structural analysis. 

Like humans, after we have been told different explanations for the same idea, we start picking these explanations apart and assembling pieces of information that we believe to be the most useful. Chat GPT probably has learned from more resources than us, which explains why it is scarily good at conversation and explaining complex ideas.

Challenges of Implementing AI in engineering design processes

ChatGPT is an impressive starting point based on what we have seen throughout this article. However, many challenges need to be overcome before we can realistically have AI that can design our structures and civil systems.  Here are the main issues we see.

Data preparation is the first and often most time-consuming step in implementing AI in engineering. This is because AI models are built on an iterative learning process, so they must have comprehensive data covering a breadth of edge cases to learn from. The more rich and higher quality data they get, the more robust the AI is. 

However, that is also where the challenges lie. Even with the digitization of many workplaces, processes, and engineering approaches, technology platforms are poorly integrated, leaving data highly fragmented. To have sufficient data to train an AI model for 

, we must first work on collecting data across numerous projects and ororganizationsLarge-scale data collection and storage can also lead to increased risks of security breaches within the storage system of AI or possible infringement of other people’s sensitive information, particularly those who may be involved with the projects from which data is collected. 

Training, an AI model for construction engineering and design, involves multiple challenges due to the complexity and diversity of data sets required. These data sets include information on building materials, construction methods, design codes, standards, and past project data. The data must represent all possible scenarios, accurate, relevant, and up-to-date to ensure optimal performance. Improving the AI model continuously requires sophisticated algorithms and techniques that can handle the complexity and variability of the data, and the ability to integrate new information and generalize learning to unseen situations.

The nature of how projects are completed, how markets apply regulation, and manufacturers supply product data means data sets in construction are highly disjointed and isolated. Engineers spend a lot of their time manually fusing information from all these sources together. 

As a result, to holistically and consistently feed an AI model the breadth of data it needs to be trained to a point where it’s effective is a massive feat.

Discover challenges in engineering data calculation and concrete mix ratios. Optimize your construction projects with valuable insights. Explore now!

ChatGPT Challenges in Engineering Data Calculation - CalcTree

Did you know that over 96% of all data captured in an engineering and construction project goes unused?  Moreover, 30% of initial data created during the design and construction phases is completely lost when the project closes out! [1] Well, this is the reality of the construction sector today. Like many industries, construction deals with exponentially increasing volumes of data from different sources. In fact, by 2025, the amount of data predicted to be in the world is an unfathomable 175 zettabytes [2], with the construction industry alone processing over 2.5 quintillion bytes of data [3]. Capturing and utilising this data is one of the key construction industry challenges right now.

Let’s paint a quick picture: some of the largest infrastructure developments require, on average, 130 million emails, 55 million documents, and 12 million workflows [1]- that’s a lot of pain points! To make matters more complex, stakeholders often use and prefer particular software and methods to manage and store their data, creating fragmented workflows and unreliable and segmented data sets.

From simple retrofits to mega-projects like the 

world’s longest underground railway tunnel

, astronomical amounts of data are created and shared throughout a project lifecycle. And with so many megaprojects happening globally, it should be no surprise that key stakeholders are looking for better ways to deliver proposals and forecast accurately. 

Studies have shown that 52% of all rework during the design and delivery of a construction project is due to poor data and communication [2]. Often, information shared between teams, internal and external stakeholders, managers, authorities, asset owners, and clients is unstructured, poorly coordinated, and hard to find. This results in an estimated 20-25% added cost on project delivery.

So, how can this data be better managed, and what kinds of steps should we expect to take forward? Well, what is needed most is more uniformity and integrity in how data is structured, managed, and distributed, so that rework, timescale blow-ups, and budget blow-outs are a thing of the past. Effective project delivery should rely more on the following principle: provide the right people with access to the right information at the right time. That’s why we need more Common Data Environments or CDEs.

Governments and key members in the construction and engineering sectors are keen to establish CDEs to achieve consistency and better data collaboration across project teams. In this article, we’ll provide a case for the wider adoption of standardized ways of structuring data and collaboration through CDEs and where there is room for improvement in their usability.

Connecting construction: what is a Common Data Environment?

So what is a common data environment? Suppose you’re an engineer working on a major civil development. Your day’s probably jam-packed with planning designs, drafting, onsite visits, gathering information, and taking phone calls. You might have to schedule meetings with clients and asset owners to ensure technical requirements are implemented and aligned with designs. You might be working with architects to exchange design solutions or with estimators and surveyors to discuss associated costs and potential design risks. You’ll probably also have to navigate through hundreds of submittals, RFIs, and an insane amount of elements in a BIM model. In short, you'll consume, provide and generate a great deal of data. Sometimes, this project data is not yet formally delivered to clients, owners, or stakeholders; when it is, it might come back to you to be reworked, changed, or manipulated. 

So far, your day is looking a lot like this:

Put yourself in anybody else shoes, and you’re still facing similar issues!

Now, add an online centralized management tool of all key digital assets that provides a single source of truth for collecting, managing, and sharing information amongst your team; your day might now look like this:

This is essentially what a Common Data Environment is. It is an online repository that combines all project data, teams, workflows, and documentation into a coordinated database. 

Generally, a CDE would establish a central source of truth for works in progress, an accessible platform where team members can easily find the information they need. The data isn’t copied or duplicated; a check-in and check-out system is facilitated, enabling version control, quality assurance, and audit trails. Members will be confident that they are always working on the latest information, minimizing the risk of rework, duplication, and mistakes.

A CDE can take many forms depending on the size and type of project; it can be a project server, extranet, or cloud-based system. In any scenario, teams and key stakeholders can consume, develop and edit approved-to-share information they require before it is formally delivered. Once approved and ready to share, data is moved to another state within the CDE where additional stakeholders can consume the information without manipulating or changing it. If edits are needed, data returns to the work-in-progress stage for updates and re-approval. Once project data is authorized and signed off, it can be issued from formal delivery, tender, milestone delivery, or construction. Teams, authorities, asset managers, and owners can access finalized and delivered digital assets here.

Data sets in the ACE industry after often extremely complex, varied, and fluid, and not all CDEs are compatible with managing all kinds of data. So industries often create an ecosystem of CDEs to meet the needs of the industry. For example, a separate CDE might be created just for Revit users to collaborate, and then revisions of this sub-CDE are exported to an umbrella CDE. Either way, organization-wide data flows should achieve interoperability among systems so that companies can efficiently store, track, manage, and transfer data without duplication.

Simply understanding a Common Data Environment can give you insight into its importance. Namely, consolidating all project data from graphical to non-graphical models in design and through to construction into one common CDE is a big disruptor. All schedules, forecasts, estimates, contracts, drawings, 3D models, handover documentation, and reports are digitized. Everyone on the team has access to it, depending on the authorization level or contractual obligations, creating a collaborative and well-managed space to share work. Here are some major benefits of implementing common data environments on construction projects:

The paradox is that many of the construction industry's challenges are obstacles to CDE adoption but are precisely what a CDE would help solve. Whilst executing projects through seamless workflows and enhanced collaboration sounds impressive, the execution is challenging due to:

CDEs are an important step forward in the construction sector; not only do they create a shared language for participants to use across a project, but they are seen as the most effective solution to the data issue that has plagued the feasibility, efficiency, and productivity of construction projects.

should be evaluated and defined in a data strategy so that all data sources and repositories are counted for and to allow interoperability among systems to optimize data flows. 

Right now, industry professionals see the value of BIM methodology in the built environment, and it has become a common and useful way to deliver construction projects. The data provided to teams from a BIM model can reduce the risk and cost of rework and poor designs by providing all key stakeholders with a visual model of a project throughout its lifecycle. A CDE platform provides extra ease, efficiency, and security to record, distribute and resolve project evolutions and challenges at a lower cost. 

New technologies are also driving changes when it comes to sustainable outcomes. Harnessing data can lead to cleaner and smarter projects. Technologies can shake up the traditional development, construction, and operation lifecycle of a building and recompose it in ways that bring sustainable benefits throughout the project lifecycle. Hot technologies, like digital twins, not only provide a visualization of physical assets but can help the construction sector visualize how sustainability evolves and what it looks like throughout all the building stages, both using predictive analysis and real-time data.  With more efficient delivery and a better-informed client, CDEs can help engineers and construction professionals bring enhanced control, productivity, and insights to projects.

Explore the power of common data environments in the construction industry. Overcome challenges and streamline collaboration. Read more.

Data fragmentation, inefficient document & project management, slow adoption, and a slew of other inefficiencies are some of the key problems facing the construction industry today. Failure to address these problems costs the global economy a whopping $1.6 trillion a year! [1].

We know that the Data Revolution is changing the organizational dynamics of many industries through decision refinement, productivity boosts, and improved administrative faculties. Like many industries, the construction industry deals with large and exponentially increasing volumes of diverse data from different sources. Yet the adoption of new data integration solutions that can help store, process, combine, transform, and manage these datasets is still in its infancy, lagging in uptake compared to other industries. But imagine the disruptive potential: allowing all stakeholders - engineers, consultants, architects, manufacturers, clients, owners, operators, subcontractors, and suppliers - to be on the same page and access reliable, real-time data.

Ok, we hear lots about ‘data sources’, we hear ‘integration’, and we’ve even mentioned ‘revolution’, so what does all this mean?

Data integration is a series of computational processes developed to retrieve, consolidate, and mix data from different sources into a single dataset. The aim is to take all of this dispersed information and turn it into something meaningful and actionable. The need to share and integrate data will grow as speed, efficiency, and reliability become an impetus for businesses and customers alike. The goal of having consistent access and delivery of data is to allow user and business applications to meet all informational and data management needs. Access within a single framework is needed across a broad spectrum, volume, and velocity of subjects, web data, structure types, and data from the Internet of Things (IoT).

Why should you care about data integration?

From a business perspective, managing enormous volumes of data through a data integration system is becoming increasingly essential. Data and data sources are widely distributed and often very fragmented. As business applications become more data-driven, they need to connect these meaningful data systems and incorporate them across the whole business structure, attaining a one-stop shop for data storage, access, and availability. Valuable knowledge and information have now become unseamed; there is continuity. With that level of knowledge transfer, organizations can enhance their working dynamics, refine decision-making, have a centralized and unified view of business insights, and boost productivity and agility. The 

 shows that 53% of companies use big data analytics to inform strategic and operational decisions and enhance business intelligence. 

If you are interested in the nitty-gritty, 

 to see how data is fed into the pipeline and examine how data from multiple sources can be integrated.

The case for a productivity revolution in the construction industry

Full-scale digitization of the construction industry is projected to lead to annual global cost savings of $1.2 trillion, 21% of which are in the engineering and construction phases alone [2].

Right now, the construction industry is like a slow-moving tortoise in the race to realize the benefits of data management. Yet, given the complexity and variety of data sets handled, the need for improved data quality management in construction far outweighs the needs in other industries. Inadequate data management can affect communication, the impacts of which can compromise safety and cost construction companies Millions in rework expenses.  It’s all frustratingly slow!

The construction sector is dealing with an exponential increase of complex and specific data from diverse disciplines in the backdrop of increasing cost pressures.  Building plans, specifications, rate of price, and day-to-day project progress throughout the life cycle of a project are examples of the kind of data the industry has to manage. Processing and extracting value from multiple sources is essential as project success is closely linked to continuous access to reliable project data.

Yet, the construction industry suffers from critical inefficiencies; the sector is mired in quality issues and site errors, inefficient project management, fragmented design processes, design mistakes, and poor worker safety. Implementing innovative technologies to address these challenges, like data integration, almost seems inescapable. Digitalizing the industry and effectively integrating data is vital in advancing the sector. This advancement also gives rise to new opportunities in sustainability, business intelligence, and innovation.

The industry is moving towards boosting productivity by implementing data technologies. Current operations in construction data platforms are driven by data exchange between structural, electrical, architectural, and mechanical disciplines. However, interconnecting these channels and achieving a unified view of project specifications remains challenging. Data across these divisions and other relational databases are usually rigid and collected inconsistently. This inconsistency and difficulty in interpreting datasets can have severe consequences on-site.

Some of the technologies currently available in the market include:

Project managers can communicate with engineers and on-site workers in real-time and provide additional site data through these technologies. The adoption of these technologies would not only untangle decision-making, minimize risks and optimize design and process automation, but it would accelerate the adoption of data integration in the construction sector. How? Well, all of the tools mentioned above generate data, and lots of it. Comprehensive integration of data from BIM with material, geographical, or personnel data from tools like VR, drones, and AR would allow the industry full-scale insight into its operations. This will streamline operations in real-time, manage on-site risks and boost productivity.

Adopting innovations, like data integration solutions, at such large scales will undoubtedly face pushback. As construction companies are slowly starting to adopt collaborative technology, its disruptive and game-changing potential in the industry is yet to be fully known. This is especially true since the application of these technologies has not been thoroughly vetted or tested on a large scale within the construction industry [3] 

Here are some prominent barriers to the widespread adoption of data integration technologies in the construction sector:

Big data integration technologies in construction

Despite the clear challenges, the economic outlook for the construction industry is favorable, expecting a 

 in both non-residential and residential sectors worldwide in 2022. So as more and more construction projects are being built, a vast amount of data will have to be handled in the future.

It represents the huge and complex data sets and information stored until now, which will continue to be acquired. Safe to say acquired data will be more complex as the industry grows and innovates. Dealing with tremendous amounts of data in construction is nothing new. However, future growth in the industry would likely be limited by the effectiveness of data management, data quality, and exploitation within the sector. The construction industry is lagging in adopting digital technologies and integrating big data. Still, as cost, safety, competition, and efficiency constraints become more challenging, the application of integration tools in the industry will need to be pursued. The optimum use of data integration through appropriate applications and systems could be the new limit for project success and innovation in the industry.

Web-based application for data integration is becoming a promising solution for reducing data complexity by encouraging data collaboration and integrity. Right now, these innovations in the AEC industry are few and far between, but there are a few start-ups that are making serious headway:

A cloud-based platform here to aid in the digital transformation of AEC companies. Speckle offers 3D data curation, versioning, and automation solutions, allowing collaboration across teams and disciplines within an integrated design workflow.

A construction tech start-up aiming to integrate all project management, financial, bidding platforms, and 

 into its unified API. Agave is working to modernize contech by allowing unified and painless data integration and dismantling data silos and fragmented systems. 

This is where we come in! We’re launching the world’s first 

multi-disciplinary calculation management platform

. We help you ensure data, designs, and calculations are always in sync. Freeing you to spend more time creating innovative, sustainable, and modern designs. To learn more, join our waitlist and 

In today’s evolving marketplace characterized by more and more incursions of data, industries, and corporations are quickly embracing the digital revolution. Many enterprises are prioritizing data integration to support their analytics and business divisions. Healthcare, financial, manufacturing and retail industries currently occupy the bulk of the market share adopting data integration tools to boost productivity. Here are some significant and new players in data integration tools:

Today's construction industry is different; it consists of shorter deadlines, changing stakeholder interests, more difficult urban environments, environmental, cost, and policy restraints, and blurred national borders. The opportunity to digitize information and workforce management could help the sector realize major benefits, including reduced labor costs resulting from improved planning, more comprehensive risk assessment and risk mitigation, worker safety and experience, and much more. The utilization of new technologies and effective ways to integrate existing and acquired surges in data because of unique and new challenges may be the new frontier of innovation in the industry.

Uncover the Importance of Data Integration for the Construction Industry. Streamline operations, enhance efficiency, and achieve success. Explore now

Importance of big data in construction industry - CalcTree

, you would notice the trend of more automated, mobile, and fluent industries. So, have you caught up with latest in cloud based construction software yet? 

The birth of the pandemic has opened the potential to work and access information from anywhere. Our favorite BIM Software, Project Management software, or your familiar Microsoft Suite are now available on the cloud. You may be expected not to send your Microsoft file through email soon, finally.

For engineers, the civil and structural engineering industry is dynamic and ever-changing. It has to adapt to a booming market, a delayed project, or a lull economy. That is where cloud based construction software or platform engineering shines in the industry with its adaptability. Still, not all companies have adopted this method over traditional software and engineering calculation design methods.

To understand the problem more clearly, and therefore the advantages of cloud based software, let’s compare traditional and cloud-based engineering methodologies.

For example, engineers have to abide by specific design codes in structural design. This determines design limitations like a wall's maximum and minimum thickness or the materials’ minimum strength.

Traditionally, structural engineers will build a model on desktop software to check if the designs abide by appropriate design codes and simulate real-life conditions like earthquakes. They will adjust or redesign structures’ components if they have not complied with the building code. Hence, engineers must repeatedly do design checks with all building components. And they have to repeat the same design code checking for new projects! Doesn’t sound very time efficient.

Cloud based engineering software makes the design process faster by automating repetitive components. Cloud companies usually design platforms that incorporate engineering calculators (by code or API), and the users only need to input the dimensions to check the design results. This, by far, will be much faster than continuing to iterate on your design model all the time manually. 

An example of an online engineering platform is CalcTree. We will soon launch our engineering platform, powered by a growing respiratory of calculations from engineers worldwide. Please join our waitlist to receive the latest updates.

Building information modeling (BIM) technology improves the efficiency of projects. You may have heard of some unique and complex constructions, but they are impossible to build without cloud based BIM software untangling their sophisticated nature. 

For instance, the Bird’s Nest Stadium in Shanghai, home of the 2008 Beijing Olympics, consists of hundreds of curved steel structures to create its unique bird’s nest shape. The production and installation of those structures were so intertwined and highly sophisticated that Arup started to implement BIM, a highly new technique at the time, to solve its technical difficulties. This enormous application indicates the future importance and potential of cloud-based software.

In short, BIM allows users to generate computer renderings of buildings and utilities with different layers of engineering design, such as structural analysis, utility planning, and geotechnical designs. The model, which contains a huge amount of data, will be used on the cloud to share, making it easier to manage the model. This enables superior project analysis, prefabrication of parts or on-time updates, and accurate quality control. Autodesk describes it as “an intelligent 3D model-based process to help professionals manage buildings and infrastructure.”

BIM can also open up several innovative possibilities in the industry that you might have heard of, such as:

This may sound obvious, but tools like Google Documents, Outlook, and OneDrive are a part of the daily cloud-based tool lists that most engineers (and other industries) use. For the same reason that you like to see immediate updates from your teammate’s work in Google Documents, you would like to see the same dynamics and flexibility in project management applications. Hence, real-time tracking is required to manage hundreds of activities and deadlines in one project. People can:

These online collaborative applications are powerful tools in the budget, timeline, and people management of construction companies. Of course, their configuration is much more complex than a normal note-taking app, and there are requirements to consider when choosing the 

Engineers might need to render and store hundreds of technical drawings and modeling files for a typical building on their computers. One 3D modeling file can range from 1 to 20 Gigabytes (GB)! So a few projects and your laptop is already out of capacity. 