10 uses cases - Artificial Intelligence and Machine Learning in Construction - by ai.business

Machine Learning use in
Construction
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USE CASE – ATMA: Autonomous TMA
Truck
The automated vehicle “learns” from the
human-driven one that is outfitted with a NAV
Module that is strapped to the roof of the
vehicle during testing. This transmits the GPS
position data called "eCrumbs" back to the
Follower vehicle, which then uses the data to
follow the exact path and speed of the Leader
vehicle at each point along the route.

ATMA (Autonomous TMA Truck):
EFFECTS OF USAGE
• Is outfitted with an electro-mechanical system and fully integrated
sensor suite, enabling Leader/Follower capability;
• This system configuration permits replicating the real-world operation;
• ATMA can follow a lead vehicle completely unmanned;
• The NAV Module can be easily unstrapped and removed from one
vehicle and installed on another if a different leader vehicle is
required.
Source: http://www.royaltruckandequipment.com/atma

USE CASE – LENS: Predictive modeling
Lens is a model-based estimating tool that ties the
Autodesk Revit Building Information Model (BIM)
to Art to Science Estimating (ASE) at the earliest
stages of a project.

LENS - Predictive modeling:
EFFECTS OF USAGE
• The platform allows users to work with any model, regardless of
modeling standards, at the earliest stages.
• Lens improves the speed in which a takeoff can be competed and
the reduction in time required to update estimates makes the
entire preconstruction.
• It helps identify items that may be normally missed due to the
possibility to see the intent of the designer as early as design
development.
Source: http://www.jedunn.com/blog/what-lens

USE CASE – Raising Efficiency of Air
Conditioning Systems in Commercial Buildings
Machine learning
techniques can be
used to predict
building A/C energy
consumption to help
with efficiently
automating the air
conditioning process.

Raising Efficiency of Air Conditioning Systems in
Commercial Buildings: EFFECTS OF USAGE
• The application models the effect of each building sensor measurement on the
A/C system energy consumption
• 3rd order polynomial support vector regression (SVR) model best predicts the
building A/C system
• Uses supervised learning algorithms to predict the amount of energy consumed to
maintain the temperature at a desirable level.
• Artificial neural networks achieve good results in predicting consumed energy in
commercial buildings and offices.
Source:
http://cs229.stanford.edu/proj2013/MahdiehMohammadiEhsani-
Y2E2BuildingEnergyStudy.pdf

USE CASE – Detecting Building Collapse in
Post-Earthquake Environments

Detecting Building Collapse in Post-Earthquake
Environments: EFFECTS OF USAGE
• For earthquakes in particular, being able to map the distribution of damage
quickly and with confidence can help channel appropriate aid to the most-
severely impacted regions.
• Accurate mapping can also aid in determining whether citizens can return
safely to their homes, so as to 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.
Source: http://cs229.stanford.edu/proj2013/BenjaminCorriganGibbsWong-
UsingLowCostRemoteSensingDataToDetectBuildingCollapseInPostEarthquakeEnvironments.pdf

USE CASE – Earthquake-Induced
Structural Damage Classifier
Developing a
structural damage
classifier using
support vector
machines.

Earthquake-Induced Structural
Damage Classifier: EFFECTS OF USAGE
• Used for predicting the post-earthquake damage state, given the building features and input
ground motion.
• 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.
• Using k-means clustering, each ground motion is categorized based on frequency content.
• The most influential feature is the correlation between the fundamental period and the
earthquake type.
• A preliminary safety evaluation of a building is possible using this damage state classifier.
Source: http://cs229.stanford.edu/proj2015/343_poster.pdf

USE CASE – Fatigue Crack Sensor

Fatigue Crack Sensor:
EFFECTS OF USAGE
• The machine learning platform uses different input feature combinations based on
sensor data that are defined and tested, and different classification methods are
utilized to determine a specimen is intact or damaged.
• The 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.
Source: http://cs229.stanford.edu/proj2015/341_poster.pdf

USE CASE – Mapillary: City planning
and inventory of roads and signage
Mapilliary uses machine learning to stitch together 3D visualizations of
photos contributed by its more than 12,000 users. The images are
available via an API.

Mapillary - City planning and inventory of roads
and signage: EFFECTS OF USAGE
• With Mapillary photos included in the strategic planning process of a city employees from all
departments in the municipality will be able to see their 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.
• Main advantages:
– Turn street photos into 3D maps within minutes
– View, edit, and extract geospatial data
– Automate hours of manual work with one click.
Source: 1) https://www.mapillary.com/ ; 2)
http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/11-
cool-ways-to-use-machine-learning/d/d-id/1323375?image_number=13

USE CASE – 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. Smart = “equal to selfawareness plus the ability to react”. (Andrew
Eastwell, Chief Executive of the Building Services Research and Information
Association)

Smart buildings: EFFECTS OF USAGE
• Smart building technology learns and anticipates the user's preferences, and altering
conditions to meet his needs more precisely and exactly than we ourselves can.
• The huge amount of data can help the smart buildings to make reactive – and even
anticipatory and personalized – real-time alterations to a building’s environment to suit its
occupants.
• The same technology can prolongue the 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.
• Smart Buildings are conceived as upgradeable due to the fact that technology changes, with
elements added in such a way that they can easily be changed as technology and the
building’s use develop.
Source: http://www.raeng.org.uk/publications/reports/raeng-smart-buildings-people-and-
performance

USE CASE – Estimating energy
performance of residential buildings
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, namely heating load (HL) and cooling load (CL), of
residential buildings.

Estimating energy performance of
residential buildings: EFFECTS OF USAGE
• Extensive simulations on 768 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 (0.51 and 1.42, respectively).
• The results support the feasibility of using machine learning tools to estimate
building parameters as a convenient and accurate approach, as long as the
requested query bears resemblance to the data actually used to train the
mathematical model in the first place.
Source: http://www.sciencedirect.com/science/article/pii/S037877881200151X

USE CASE – Improving BEMS (Building
Energy Management)
The 3rd wave of
innovation will
take this analysis
concept even
further to
“optimization”.
(Mike Zimmerman,
Founder of
BuildingIQ)

Improving BEMS (Building Energy
Management): EFFECTS OF USAGE
• 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 (DR) events.
• The SaaS (cloud based) 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.
Source: http://www.memoori.com/the-future-impact-of-machine-learning-predictive-analysis-on-
building-energy-management/

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