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Perhaps you might read our previous articles about influence of AI in agriculture and farming. Construction is an excellent example of industry that will be affected the most from a replacement with automation. AI could save construction businesses money if it becomes smart enough to determine price variants in companies spending for construction materials or hiring engineering companies.
We created a list of real use cases that will shape construction industry in near future. It means that driver can be removed from a vehicle in various dangerous situations. This technology is being adapted to the roadway construction industry. ATMA can follow a lead vehicle completely unmanned and NAV Module can be easily unstrapped and removed from one vehicle and installed on another if a different leader vehicle is required.
This platform allows users to work with any model, regardless of modeling standards, at earliest stages, improve speed in which a takeoff can be completed and reduce time required to update estimates during entire pre-construction process. It also helps identify items that may be normally missed due to the possibility to see the intent of a The influence and effects of artificial intelligence as early as design development.
Also supervised learning algorithms can be used predict the amount of energy consumed to maintain the temperature at a desirable level while artificial neural networks can achieve good results in predicting consumed energy in commercial buildings and offices.
Detection potential building collapse in post-earthquake environment Most earthquakes involve widespread damage, ongoing aftershocks and losses in billions of dollars. With help of artificial intelligence and low cost remote sensing data you can detect building collapse in post-earthquake environment.
Being able to map the distribution of damage quickly and with confidence can help locate appropriate aid to the most severely impacted regions. Accurate mapping can also aid in determining whether citizens can return safely to their home. In addition it can prevent casualties from delayed building collapses.
Using the machine learning techniques developed, future disaster relief professionals might be able to use a more limited field-based damage assessment, in combination with remote-sensing-based data, to identify highly damaged areas more quickly and at lower cost.
Earthquake-induced structural damage classifier Developing a structural damage classifier with support vector machines can help with prediction of post-earthquake damage state, given the building features and input ground motion.
Classifier can also be used for accelerating post-earthquake damage evaluation of critical buildings. This will allow faster recovery time and decrease financial losses expected from downtime and repair.
With k-means clustering, each ground motion is categorized based on frequency content but most influential feature is the correlation between the fundamental period and the earthquake type. Fatigue crack sensor Fatigue crack sensor uses different input feature combinations based on sensor data that are defined and tested, and different classification methods to determine a specimen is intact or damaged.
Sensor data is acquired from steel specimen using a high-frequency fatigue crack sensor. The raw sensor data is pre-processed so that several features representing meaningful information of sensor data can be extracted. The images are available via an API.
With help of Mapillary city employees from all departments in the municipality will be able to see future investment areas combined with up to date photos on the map. The Mapillary mobile application can be used along a selected railway line to complete field observations and quality controls.
With mapillary every user can turn street photos into 3D maps within minutes, view, edit, and extract geospatial data and automate hours of manual work with one click. Data-enabled machine learning with smart buildings Data-enabled machine learning creates a smart building, whose defining feature is the ability to be proactive in making appropriate changes to services on behalf of its users.
The same technology can prolong time that elderly people can remain in their own homes by allowing remote monitoring of health through blood pressure and heart monitors that note behavior patterns and highlight any change that might indicate a problem.
Estimation of energy performance of residential buildings Artificial intelligence can be used for developing a statistical machine learning framework to study the effect of eight input variables relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution on two output variables: Extensive simulations on diverse residential buildings show that we can predict HL and CL with low mean absolute error deviations from the ground truth which is established using Ecotect 0.
The results support the feasibility of using machine learning tools to estimate building parameters as a convenient and accurate approach, as long as query bears resemblance to the data actually used to train the mathematical model.
Building energy management improvement Managing the energy and other needs in buildings efficiently and intelligently can have considerable benefits. Next to energy management, the system can control and monitor a large variety of other aspects of the building regardless of whether it is residential or commercial.
The system combines an energy model of the building with external data such as weather forecasts and energy pricing signals to automatically write set points for the BEMS and execute Demand Response events.
The SaaS software works with the buildings existing BEMS and utility demand response systems, incorporating weather forecasts, occupant comfort, utility prices and demand response signals into its optimization algorithms.Basics and Overviews.
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National Security-Related Applications of Artificial Intelligence Introduction. There are a number of direct applications of AI relevant for national security purposes, both in the United States and elsewhere.