Civil Construction :
- For estimating the percentage of soil moisture content and further classifications.
- In the structural engineering field machine learning can be applied to detect damages using sensory or image data, identifying it’s location and extent.
- Improving productivity by reducing idle time.
- For predicting maximum dry density and optimum moisture content in concrete.
- Using image recognition for proper site monitoring, including aspects of safety and dangerous working conditions.
- Identifying gaps and requirement of materials to cover the tasks without delay.
- For travel time prediction and sign AI optimization in transportation engineering.
- Efficient planning, designing and managing of infrastructure using Building Information Modelling (BIM).
- Utilizing Artificial Neural Network for predicting properties of concrete mix designs.
- To monitor activity in the construction site and predicting changes in the costing based on raw material market rates.
- To analyse settlement of foundation and slope stability.
- For monitor real time structural health of the building, giving warnings on when and where repair is required.
- Helping in tidal forecasting to aid construction in marine environment.
- Reducing errors in the project by automatic analysis of data.
- To develop site layouts and predict risks as part of project management.
- Finding a solution for damage related to pre-stressed concrete pile driving in foundation engineering.
- To solve complicated problems in different stages of the project.
- To make decisions in the design field.
- In the construction waste management domain and handling of smart materials.
- For expert monitoring and optimization if costs in the work system.