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It could be argued that the growth of Artificial Intelligence (AI) is comparable in a number of ways to the industrial revolution. The industrial revolution profoundly changed the way in which production and service operations were based. With different technological advancements impacting most STEM industries, flying drones above construction sites to analyse telemetry data has become more commonplace. Alongside the applied concept of visualising project specifications beforehand with virtual reality. That being said, the stance that Artificial Intelligence would take in construction is still being moulded and given shape, particularly in recent years.
Now among the current main purposes of AI is the manipulation and compartmentalisation of large sets of raw data. However, the effective application in regard to Construction and Artificial Intelligence is still relatively unknown, uncharted and up for debate. Therefore, comprehending the amount of data produced is essential for the practice of modelling, project maintenance, and drawing visualisation. However, the majority of construction professionals, such as quantity surveyors, engineers and architects are not employing these software’s and technologies to their advantage for their own or the customers’ projects.
The primary hurdle in these situations is establishing solid foundations for handling the type of telemetry data collected to guarantee that diverging models originating from the sub-teams do not intersect and clash with each other. Machine learning is a leading form of design which is consistently generative, in order to pinpoint and best avert potential overlap or clashes between various generated models within different teams in the design phase.
Certain software programs which adopt algorithms centred around machine learning are optimal for examining possible solutions and formulating alternate designs. This method is tailored to explicitly manufacture 3D models of plumbing, mechanical and electrical systems whilst concurrently ensuring the pathways for the MEP based systems do not conflict with the building architecture, alongside ascertaining the ideal results from each iteration.
An abundance of companies have been investing into self-operating machinery and hardware to carry out more menial and repetitive tasks, which could be better handled if the process was automated. In many cases, this would radically boost workplace efficiency in comparison their human counterparts, allowing for more time and effort to be put into more complicated and unique tasks, which need to be carried out by construction workers. In an ideal situation, large or small scale excavation work and preparation would be carried out by autonomous or semi-autonomous heavy equipment and machinery which, with the help of a human programmer, could be constructed and produced to exact specifications.
Furthermore, managers and project leaders would be able to monitor work progress in real time even if they are off site. By installing facial recognition software, onsite cameras and bio-metric security in tandem with congruent technologies to gauge workplace productivity and conformance to construction guidelines.
There is always a particular degree of risk involved with a construction project. Whether it falls under areas such as health and safety, time and scheduling risks, compromises in quality or risks and drawbacks in expenditure. The severity of these risks is often proportional to the size and scale of the project. Especially with a large number of sub-contractors working together in different sectors. To combat these potential risks and hazards, machine learning solutions and AI could allow contractors to perform risk assessments and carry out extensive review of job site safety and prioritisation. This in turn would permit the project workers to reallocate their constrained time and resources on the highest risk concerns.
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