513335690-AI-applications-in-civil-engg.pptx

1. AI applications in Civil Engineering
By. Piyush Bhandari

2. 1. Structural Design
• AI-based applications will enable the designers to collaborate with computers to
create complex designs quickly. Designers can utilize software tools to input different
design goals and criteria. Software solution utilizes AI to generate options for
designers to select from alternatives and make necessary changes as needed.

3. 2. Estimation:
• Generative design combined with data on costs and schedules of similar projects will
provide great value for estimators. Machine learning systems can analyze the data
from similar projects to develop preliminary cost and schedule estimates. Combining
AI with BIM allows estimators to develop accurate estimates with greater accuracy
within a quicker turnaround time.

4. 3. Safety management
• AI-based applications utilizes visual processing algorithms as a important risk
monitoring and prevention tool for safety managers. Photos and videos from the
jobsite are analyzed for safety hazards such as missing workers, not wearing
appropriate PPE gear etc. Similar to project managers, many safety managers are
managing multiple projects and cannot be at the jobsite all the time. However, the
field team is capturing photos at the jobsite. Safety monitoring solutions that utilize
AI works will scan large amounts of photos and quickly. It will identify workers and
instances that are not following safety protocol.

5. 4. Project management
• Project managers utilize autonomous devices such as drones, sensors, and cameras to
monitor job site activity. AI-based applications will utilize data to measure the
quantity of materials consumed by analyzing the job site in 3D. The learning
algorithms track the progress in real-time against original plans, budget, and
schedule. Project managers use the progressive information to track labor
productivity and make adjustments to keep the project on schedule.

6. 5. Construction foremen
• The AI-based applications are used to measure the quantity of materials used. The
same learning algorithms will measure the quality of work. The 3D model captured
by drones and cameras is compared against the original design to detect any errors or
inconsistencies. Foreman can receive timely alerts to immediately work on
corrections before the scope for rework increases.

7. 6. Water Resources Engineering
– For Time series Prediction
• Rainfall-runoff prediction
• Rainfall forecasting
• Stream-flow prediction
• Reservoir inflow prediction
• Reservoir operation

8. 7. Irrigation engineering
• Evaporation prediction
• Evapotranspiration prediction
• Irrigation return-flow prediction

9. 8. Structural Analysis
• Automated conceptual design of structural systems
• Structural dynamics involving Earthquake
• Non-linear analysis of plates
• Detection of damages
• Vibration analysis

10. 9. Transportation Engineering
• Traffic flow simulation
• Automatic signals
• Priority of highway maintenance

11. 11
10. Geotechnical Engineering
• Hydraulic conductivity
• Soil thermal resistivity
• Strain-rate dependent behaviour of soils
• Prediction of settlements during tunneling
• Predict settlement of shallow foundation
• Assessment of damage of pre-stressed piles
• Capacity of piles in cohesionless soils

12. Applications of ANN
1. Rainfall Forecasting:
– Multi layered feed forward network can be used along with Back Propagation
training algorithm for conversion of the remote sensed signal into rainfall rates.
It is further used to determine the runoff for a given river basin.
– ANN technique can be applied for rainfall-runoff modelling. Daily runoff can
be forecasted by giving input of daily precipitation, temperature and snowmelt
into multi layered feed forward network with Back propagation algorithm.
– Hourly rainfall values can be disaggregated into sub hourly time increments
with the help of ANN. Feed forward back propagation steepest descent learning
algorithm is used along with unipolar activation function.

13. 2. Runoff Prediction:
• ANN with linear and non-linear regression techniques can be used for spring runoff
prediction
• For real time forecasting of hourly flood runoff and daily river stage and to the
prediction of rainfall sufficiency for India.
• Runoff Forecasting can be done with back propagation, conjugate gradient and
cascade correlation training algorithm.
• ANN model can predict both runoff and sediment yield on a daily as well as
weekly basis from simple information of rainfall and temperature.

14. 3. Reservoir operation:
• ANN based model can be used to monitor the monthly inflow data series and their
performances. ANN is found better for predicting the high flows. Also ANN is an
powerful tool for input-output mapping and can be used for reservoir inflow
forecasting and operation.
• ANN based simulation optimization can be used for reservoir operation.
• For multivariate reservoir forecasting, different types of neural network
architectures, i.e. input delayed neural network (IDNN) and a recurrent neural
network (RNN) and multi layered perceptron (MLP) are used
• Dynamic programming based neural network model is used for optimal reservoir
operation.

15. 4. Stream flow prediction
• Flows in streams are main input for design of any hydraulic structure or
environmental impact assessment.
• Cascade correlation algorithm is used instead of designing and trying different types
of architectures for predicting daily stream flow data. This type of algorithm uses
incremental architecture in which training starts with minimal network and goes on
increasing size as proceeds.
• To forecast level of water in river. Different types of algorithms like hack
propagation, conjugate gradient and cascade correlation can be used. Among them,
Cascade correlation algorithm is the fastest for the training of the network.
• To establish the rating curves. Three layered back propagation training algorithm is
used. ANN is significantly better than conventional curve fitting techniques.
• For forecasting river stage and for the sensitivity analysis

16. 5. Estimation of evapotranspiration
• For estimation of daily grass reference crop evapotranspiration. Multi layered
feed forward network with back propagation is normally used. Single hidden
layer is capable for developing non-linear relationship between climatic variables
and corresponding evapotranspiration.

17. 6. Draught Analysis
• A draught is generally defined as an extreme deficiency of water available in the
hydrologic cycle over an extended period of time. Draught forecasting plays a vital
role in the control and management of water resources systems.
• ANN is used to forecast draught. Conjunctive models are used to significantly
improve the ability of ANN to forecast the indexed regional draught. Three layered
feed forward network with back propagation training algorithm is used. Accurately
predicted draughts allow water resources decision makers to prepare efficient
management plans and proactive migration programs that can reduce draught
related social, environmental and economic impact significantly

18. 7. Soil water storage
• ANN is used analyse the soil water retention data. A three layered feed forward
network is used in input layer, one neuron represents the matric potential values and
the output neuron represents corresponding moisture content. Hidden layer having 5
neurons and sigmoid transfer function is also used. Then Back propagation training
algorithm is used.

19. 8. Flood Routing
• Information theory and neural networks are used for managing uncertainty in flood
routing. Parallel ANN model uses state variables, input and output data and
previous model errors at specific time steps to predict the errors of a physically
based model. ANN models can remove the errors of physical based models also
reduce the prediction uncertainty.

20. 9. Model Drainage Pattern
• ANN is used to automatically determine the drainage pattern from digital elevation
model (DEM). Three-layered network with back propagation algorithm is used for
training

21. 10. Classification of River Basins
• Pattern clustering and pattern mapping capabilities of ANN are used for classifying
river basins. An unsupervised ANN architecture viz. Adaptive Resonance Theory
(ART) is used for pattern clustering that is grouping of basiss of hydrological
homogeneity. Multi layered perceptron is used for pattern mapping.

22. 11. Tidal Level Forcasting
• Tidal level record is an important factor in determining constructions or activity in
maritime areas. Back propagation neural network is used to forecast the tidal level
using the historical observations of water levels. However, their model is useful only
for the instant forecasting of tidal levels, not a long-term prediction.

23. 12. Earth Retaining Structures
• Neural network model is used to provide initial estimates of maximum wall
deflections for braced excavations in soft clay. The input parameters used in the
model are the excavation width, soil thickness/excavation width ratio, wall stiffness,
height of excavation, soil undrained shear strength, undrained soil modulus/shear
strength ratio and soil unit weight. The maximum wall deflection is the only output.
Neural network model is a time-saving and user friendly alternative to the finite
element method.

24. 24
Application of Genetic Algorithm (GA)
1. Pipe network
• Design of networks, and analysis
2. Ground water management problems
• Quantity and quality management models

25. 3. Reservoir operation
• For Single purpose single reservoir
• For Multi-purpose single reservoir
• For Multi-reservoir systems
• For Multi-purpose multi-reservoir systems