The ope­ra­tio­nal life­span of many sub­way tun­nels in Ger­ma­ny requi­res inten­si­ve moni­to­ring and regu­lar inspec­tions to ensu­re the struc­tu­ral inte­gri­ty and func­tion­a­li­ty of the­se faci­li­ties. Until now, the­se inspec­tions have been car­ri­ed out manually—a time-con­sum­ing and ris­ky pro­cess that not only demands signi­fi­cant resour­ces but is also pro­ne to inac­cu­ra­ci­es. This is whe­re the inno­va­ti­ve rese­arch pro­ject Robo­TUNN comes into play. 

Tog­e­ther with rese­arch insti­tu­tes from RWTH Aachen and the Uni­ver­si­ty of Frei­burg / Fraun­ho­fer, as well as other indus­try part­ners, Stat­Soft will work over the next three years to ele­va­te auto­ma­ted inspec­tion, dama­ge detec­tion, and pre­dic­ti­ve main­ten­an­ce of infra­struc­tu­re to a new level. 

Project Goal: Autonomous Inspections and Digital Twins 

Laun­ched in August 2024, the Robo­TUNN pro­ject aims to auto­no­mously inspect sub­way tun­nels and detect dama­ge in real-time using mobi­le robo­tics and arti­fi­ci­al intel­li­gence. The data coll­ec­ted will gene­ra­te con­sis­tent digi­tal twins (DT), enab­ling pre­dic­ti­ve main­ten­an­ce manage­ment. 

By uti­li­zing robo­tic sys­tems, com­pre­hen­si­ve assess­ments can be con­duc­ted in real-time, and dama­ge pat­terns can be cap­tu­red effi­ci­ent­ly and accu­ra­te­ly. The­se con­sis­tent digi­tal twins not only offer a pre­cise repre­sen­ta­ti­on of the cur­rent con­di­ti­on of the tun­nels but also ser­ve as a foun­da­ti­on for pre­dic­ti­ve main­ten­an­ce, ther­eby mini­mi­zing safe­ty risks and redu­cing main­ten­an­ce cos­ts. 

StatSoft’s con­tri­bu­ti­on focu­ses on two are­as:

1. We will levera­ge our exper­ti­se in inte­gra­ting hete­ro­ge­neous data sources and data fusi­on to crea­te opti­mal con­di­ti­ons for the ana­ly­ti­cal and pre­dic­ti­ve use of the data.
2. We are respon­si­ble for the AI mode­ling for pre­dic­ti­ve main­ten­an­ce. This will be deve­lo­ped based on the digi­tal twin, a dyna­mic model of the tun­nels. 

Research Partners and Funding through mFUND 

The pro­ject is fun­ded as part of the mFUND inno­va­ti­on initia­ti­ve by the Fede­ral Minis­try of Digi­tal and Trans­port (BMDV). It brings tog­e­ther a ran­ge of high­ly spe­cia­li­zed part­ners, inclu­ding RWTH Aachen, the Fraun­ho­fer Insti­tu­te for Sus­tainable Tech­ni­cal Sys­tems Frei­burg (INATECH), and com­pa­nies such as LAT Funk­an­la­gen-Ser­vice GmbH, ISAC GmbH, albert.ing, and Stat­Soft GmbH. 

A Significant Step for Infrastructure 

The Robo­TUNN pro­ject will explo­re and test how modern tech­no­lo­gies such as AI and robo­tics will shape the future of main­ten­an­ce and infra­struc­tu­re moni­to­ring. By com­bi­ning auto­no­mous inspec­tions with digi­tal twins, the way tun­nel faci­li­ties are main­tai­ned and moni­to­red could be fun­da­men­tal­ly trans­for­med, paving the way for safer and more effi­ci­ent pro­ces­ses.