Project Description

Predictive Maintenance – Forecasting the road condition of the highway A70 and Visualizing in ArcGIS

Project scope

For a road builder it is essential to plan measures for the maintenance of the infrastructure based on the respective roadway, building condition and their development. Predictive analysis can lead to monitoring of public infrastructure hence cost efficiency in maintenance. On the other hand, the overall development of public infrastructure can be better monitored.

Objective: Develop an artificial intelligence solution to forecast highway conditions using neural networks on 4 characteristics – general road roughness, lengthwise roughness index, grip of the road at 80 km/h driving speed and the damage level of the highway due to cracks.

Data Sets

The Bavarian State Ministry of Housing, Building and Transport has provided historical A70 highway (Schweinfurt, Bamberg, Bayreuth) condition assessment data from the years 2009, 2013 and 2017. In addition, the traffic density data of the A70 highway was also provided for the years 2005, 2010 and 2015.

Challenges & solutions

The data provided were collected at three different points in time, which resulted in a significant pro-portion of missing values in the aggregation.

First, the traffic density data was merged with the high way condition data, variables with >50% miss-ing values were excluded from the downstream modelling process. To meet the requirement of neural networks, that missing values should be imputed before the modelling. 4 predictive performance models were compared: Neural Network, LSTM (Long term short memory), CNN (Convolutional Neural Network) and the stacking model of LSTM + CNN. The R-square of the stacked LSTM+CNN model was the highest and shows the best model performance.

Project outcome

The combination of LSTM + CNN model was successfully used to predict the road condition variables, with 60% accuracy. his prediction was visualized in ArcGIS showing the degree of the road condition.

Predictive Maintenance
Predictive Maintenance Supper & Supper A70

Category

GEO AI
Infrastructure
Predictive Maintenance
Spatial temporal Analysis

Technologies

Deep Learning
LSTM-CNN

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The inclusion of artificial intelligence in the analysis of geodata enables us to design management processes in a targeted and efficient manner. Close cooperation with efficient partners in the field of data analysis opens up completely new avenues. It creates new levels of knowledge that reduce potential project risks and further advances profitability in project management.

Roland Degelmann
Head of Digitization
Bavarian State Ministry of Housing, Building and Transport

Category

GEO AI
Infrastructure
Predictive Maintenance
Spatial temporal Analysis

Technologies

Deep Learning
LSTM-CNN

Download

Social Sharing

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Contact

Stefanie Supper
CEO