Construction startups are looking at providing the building market with digital tools. Construction was always on the periphery of available human materials. Use of most cost-effective and reliable building components has been crucial to building throughout human progress. Although some constructive paradigms have changed in the 20th century the general approach to optimal use of resources has not. However, it may seem that this approach only concerns itself with physical resources and engineering. The general landscape of the industry is slowly moving toward applying as much computer optimization tools as possible in its strive for development.
Unfortunately, the construction industry is falling behind. The reasons are myriad:
1. Construction industry is not as glamorous as an app or website development;
2. it is populated with over-specialized professionals;
3. it is predominantly considered a physical work rather than an optimizable metric etc.
On the other hand, construction is more than a physical arrangement of building blocks. Everything from human management to certification, from developing blueprints to cost evaluation is subject to a digital quantitative approach. Pessimism aside, there are some firms that are employing machine learning algorithms to construction cost evaluation, and using 3d max in making blueprints is the industry standard. Yet still, this industry is falling behind.
In this article, we would like to focus on some general applications advanced computing technologies can have in the construction industry. We will consider everything from physical applications to human relationships applications. We will provide relevant examples of both.
What are digital quantitative tools?
Broadly, digital quantitative tools are any technologies that allow for automatic work on counting tasks by a computer. This kind of tools is widely used in connection with qualitative ones. Different industries are trying to optimize their workflows, data storages and user experiences by the means of modern digital tools. Among prominent industries and spheres where these tools are applied are data-driving marketing, automotive industry, and chemical engineering. One of the most applicable usages of quantitative tools is conducted by essay writing service. Browse around this site to find out how they combine qualitative and quantitative analysis for checking out and verifying massive datasets in their internal storage.
Now, most commonly there are two techniques used- machine learning and artificial intelligence. Machine learning is a collection of algorithms that are meant to deduce any correlations that are present in any collection of data. For example, Miguel Azzaro proposes to use linear regression ( a certain type of numerical algorithm) to make an evaluation on the speed of construction. This approach allows one to determine the time it will take to construct some objects, depending on any criterion- cost, volume, material, etc. In fact, linear regression is probably the simplest machine learning algorithm out there.
People also propose to use classification both in developing pricing strategies and in choosing the right construction materials. Classification algorithms are conceptually more complex than linear regression, but there are a bunch of tools that can allow any user to apply them. Overall, classification algorithms, unsurprisingly, classify things. One can tell the algorithm what sort of things to evaluate according to certain features and it will break these features down into relevant effective categories. An example of this in the industry is an automatic updates of city maps. This algorithm automatically reads of the data about a building and updates a relevant interactive map where this data can be stored. Google uses this in its maps, but a more relevant application would be in technical maps for architects. Such maps would have all of the relevant engineering details soil content, plumbing information, electrical circuits, compositions of surrounding buildings and so on. Having these maps is a must in the industry now, but making them automatic will increase efficiency and make them fool-proof.
Obviously, these examples do not reveal the best of machine learning in construction, because they do not immediately ease the creation of structures. This brings us to the last example, probably the most sought after. Building Information Modelling (or BIM for short) is the hot trending topic. Basically, unlike the other methods we talked about, this is not a singular algorithm. It is a system of algorithms that collects all sorts of data about your construction: from blueprint parameters to soil density in the selected area, and give you information regarding the building as it could be. This data includes risk factors, balance factors, estimated time of exploitation and so on and so forth.
It is not surprising this system is trending now, as the success of one company can show. The main idea is that with this technology the information about architectural or economic mistakes will not be collected as collateral to the construction process. In fact, this allows for a full structural evaluation prior to the physical manifestation of building. Using a BIM a team can select the actually most likely prototype to build thus saving on time, money, and even workers health.
Artificial Intelligence in Construction
Artificial Intelligence is a hot topic in all industries now. It is a semi-mystical force that drives modern human progress. It is scary and confusing. It is so confusing that it is often ambiguated with machine learning. Yet nowhere is this distinction so prevalent as in digital constructions technologies.
We have discussed machine learning, and now we will contrast it with artificial intelligence. Here is one application of artificial intelligence in construction. It can be seen clearly from this App that here artificial intelligence performs tasks akin to tasks that a human architect does. The algorithm is fed certain parameters of the project, and it gives various arrangements of buildings and predicts all the drawbacks of this particular model. It evaluates water damage, sustainability issues, unexpected cost increases, etc.
The difference with any machine learning algorithm is that this sort of system does not give people numerical data it gives a completed prototype for a project will a full qualitative breakdown of its pros and cons. This system is much more complex, but also has more possible room for error. Obviously, its maintenance is much higher and it requires a very specific set of skills from its development team. The same goes for any artificial intelligence startups. Their maintenance costs are enormous, and for now, their product is not of a certain quality.
There is a room for technological startups in construction. In fact, there are almost certain expectations for these startups to be profitable. Yet when starting on a course of making a digital quantitative service for building the entrepreneur must determine which type of service they will provide. We have considered two main types: machine learning and AI. It seems that machine learning is a more sought-after alternative. It is theoretically more mature and provides a more reliable product than artificial intelligence. On the other hand, artificial intelligence is a glossier marketing concept. Hence, if an entrepreneur is ready to provide a serious capital to the development than AI may present huge potential benefits in terms of results and profits.
About the Author:
Jenifer Lockman graduated from UCLA majoring in Journalism. From the very beginning of her career, she was spending hours with her laptop writing on various topics, investigating lots of the unknown or unclear items, etc. Her main goal was to master skills and assist other journalists with the multiple obstacles they face during the writing process. You can reach Jennifer here https://www.linkedin.com/in/jennifer-lockman-168508127/