An Omnidirectional Aerial Manipulation Platform for Contact-Based Inspection

This paper presents an omnidirectional aerial manipulation platform for robust and responsive interaction with unstructured environments, toward the goal of contact-based inspection. The fully actuated tilt-rotor aerial system is equipped with a rigidly mounted end-effector, and is able to exert a 6 degree of freedom force and torque, decoupling the system's translational and rotational dynamics, and enabling precise interaction with the environment while maintaining stability. An impedance controller with selective apparent inertia is formulated to permit compliance in certain degrees of freedom while achieving precise trajectory tracking and disturbance rejection in others. Experiments demonstrate disturbance rejection, push-and-slide interaction, and on-board state estimation with depth servoing to interact with local surfaces. The system is also validated as a tool for contact-based non-destructive testing of concrete infrastructure.Comment: Accepted submission to Robotics: Science and Systems conference 2019. 9 pages, 12 figure

Corrosion diagnosis of reinforced concrete structures using autonomous robotic inspection systems and artificial intelligence

The infrastructure in industrialized countries, which relies heavily on reinforced con- crete (RC), is aging. Over time, corrosion of the steel reinforcement, the most common degradation mechanism, becomes more likely and may cause structural and prema- ture damage to the structure. In order to ensure that the large stock of infrastructure is managed safely, economically, and sustainably, engineers require more accurate diag- noses of the prevailing condition. Currently, routine inspections of RC structures are frequently limited to their simplest form, a visual inspection, which undoubtedly has severe drawbacks in terms of early corrosion detection. Non-destructive testing (NDT) methods could provide a more accurate diagnosis of the condition of structures, but the implementation in engineering practice on a routine basis is hindered by high costs and laborious measurements, especially at locations that are difficult to access. In addi- tion, the lack of established protocols to analyze inspection data is another challenge, particularly when data from different types of NDT methods are combined. The use of artificial intelligence (AI) can help overcome challenges with regards to inspection data analysis, thereby, improving efficiency, detection accuracy, and reliability through data fusion and removing subjective biases. To fully automate the inspection process, advances in robotic systems should be integrated into the inspection process together with sensors capable of self-assessing their functionality to collect data, especially with NDT methods. This thesis highlights different opportunities to integrate developments in sensor technology tailored for autonomous inspections with advanced robotic systems. Data analysis methods based on AI methods and geostatistical analysis were developed to improve several aspects of condition assessment and prediction of the (remaining) service life compared to the current methodology. Two new sensors were developed for the non-destructive contact-based condition as- sessment of RC structures. These sensors were specifically designed for use on flying robots and tailored for autonomous data acquisition. Each sensor allows for the com- bined measurement of two essential parameters in the condition assessment: the half-cell potentials (HCP) of the reinforcement and the concrete resistivity. One of the sensors is in the form of a point sensor that allows for the continuous moistening of the sponge (to ensure an electrolytical connection) and self-assessment of its functionality. The other comprises lightweight sensors in the shape of wheels to enable autonomous movement and increased data acquisition speed. Laboratory tests with both sensors were validated with data acquired manually. To examine the usability of the point sensor on-site, the point sensor was mounted on the flying robot and successfully tested on a bridge under realistic conditions. The results demonstrate the potential of the flying corrosion inspec- tion robot to autonomously collect data using multiple NDT methods on RC structures, possibly reducing inspection costs in the future. A novel technology for the non-destructive inspection of buried RC structures was developed, which may in the future evaluate the corrosion state of steel reinforcement of cantilever retaining walls, especially in structurally critical areas of the working joint. The measurement system comprised of a probe designed to reliably establish contact with the soil to take accurate HCP measurements without the immediate supervision of a human operator. This probe was combined with steered horizontal drilling technology to move the probe through the underground and incrementally place it close to the buried RC surface. In contrast to existing methods that are often destructive and with limited local information, the proposed solution overcomes spatial limitations and provides a more consistent and trustworthy corrosion inspection tool. Successful field tests support the promising approach for this novel NDT method for the corrosion assessment of cantilever retaining walls. This thesis presents new methods to improve the analysis of inspection data to enhance the diagnosis of structures by using geostatistical methods and AI. A geostatistical work- flow was proposed to investigate the effects of spatial variability on the interpretation of HCP mapping data during the condition assessment. The workflow distinguishes between long-range and short-range variability and mathematically expresses them as a trend and residuals. The results of this proposed workflow could help optimize the grid spacing or contribute to modeling deterioration processes using random fields. AI meth- ods were used to analyze a large dataset composed of inspection data from several NDT methods to locate the corroding reinforcement, to detect damage in surface images, and to assess hydrophobic treatments using active reflectance spectroscopy (ARS). Although the detection accuracy of neural networks was found to be high in training datasets to detect surface damage, it needs to be improved to support or partially replace visual inspection in engineering practice. The novel data analysis methods developed to locate corroding reinforcement showed better detection accuracy than existing approaches, making the analysis more reliable and less dependent on the engineers’ experience. ARS, combined with the developed data analysis methods, has the potential to detect hydrophobic surface treatments on RC surfaces on-site, making it a promising NDT method for the future. In addition to improving the assessment of the present condition of the structure, this thesis presents a method to strengthen the prediction of the service life of concrete structures using AI-based crack detection on surface images for structural parts exposed to chlorides. This method might be used after regular visual inspections or extended to be fully probabilistic. NDT methods offer a more accurate diagnosis, but are currently hindered by high costs, laborious measurements, and limited accessibility. This work presented multiple en- hancements to the current inspection methodology of RC structures. The individual parts of this work aimed to enable the automation of condition assessments, from capturing data with robotic systems and novel sensors tailored for autonomous measurements to data analysis with AI methods and forecasting the condition over time. These develop- ments have the potential to greatly improve the diagnosis of structures and ultimately reduce the negative impact of repairs on the budgets of infrastructure owners, the end users, and the environment

New mobile underground inspection technology device for retaining walls

There is a lack of reliable and efficient inspection technologies for cantilever retaining walls. It is well documented that the reinforcing steel at the lower end of the backside (in contact with the back filling) near the foundation of cantilever retaining walls can corrode. In 2012 a collapse of such a wall caused a fatal accident. Corrosion at this location presents a twofold problem: First, inspecting this part of the structure is virtually impossible or can only be done as random spot-checks (e.g., with several drilling cores), but still at high inspection costs. Second, this location in the structure is critical because of high tensile stresses in the steel reinforcement, and corrosion-related steel loss can cause sudden failure of the wall without a considerable deflection. In addition, random-spot checks bear the risk of overlooking corrosion damage because the inspected length in comparison to the complete wall is very limited. Hence, an inspection method that can overcome these limitations is needed. Considering the ageing of our infrastructure, such a method will be appreciated in the near future, especially in Alpine and mountain regions. In an interdisciplinary project, a non-destructive inspection method for underground corrosion detection for retaining walls with drainage pipes has been developed and tested in experiments in the laboratory as well as in a first field trial. The measurement results on a 26 m long cantilever retaining wall showed similar results compared to analyses of two core drillings. The method is based on well-proven half-cell potential mapping and is thus able to indicate the actual corrosion state. The developed method yields promising results but needs further development for the commercial application on such walls.ISSN:1435-493

Inspecting the corrosion state of underground reinforced concrete structures

Unnoticed corrosion in underground reinforced concrete structural members – such as foundations, retaining walls, or piles – may severely threaten the integrity of structures. However, condition assessment of the ground-buried structural parts is challenging, because the areas of interest are hardly accessible for visual inspection or non-destruc- tive testing. An example of particular practical relevance is reinforcement corrosion at the back-side of the lower end of cantilever retaining walls, near the construction joint between stem and heel of the base slab. The collapse of a cantilever retaining wall in Austria was a tragic reminder of the dangers of unnoticed corrosion. The drawback of current inspection methods is that they are laborious and costly, but still, only a tiny fraction of the structure can be inspected. Considering that the degree of corrosion can vary significantly along a structure, such local information includes a risk that corrosion elsewhere remains undetected. A novel inspection system is proposed here, combining the well-proven half-cell potential measurement technique with steered horizontal underground drilling technologies. With this approach, a tailor-made probe is brought in proximity to the concrete surface in the soil and electrochemical measurements are performed to characterize the corrosion condition. The main advantage is that virtually the entire length of the structure can be inspected, thus overcoming the limitations of highly local inspection. Moreover, the proposed technique includes a method to constantly monitor the functionality of the potential measuring probe, based on electrical resistance measurements. The feasibility of the approach was confirmed in laboratory experiments on a mortar block in soil. These findings were confirmed in a field experiment. The results suggest that local corroding zones of practice-relevant size can be detected for a distance between the reference electrode and the steel surface of at least 25 cm. On the basis of this work, underground corrosion inspection of cantilever retaining walls is considered feasible, and the development of similar technologies as the one proposed here may in the future considerably enhance condition assessment of structures buried in the ground

Flying corrosion inspection robot for corrosion monitoring of civil structures – First results

Potential mapping permits an early detection of corrosion and has major advantages over a purely visual condition assessment. The current manner of assessing the corrosion state of reinforced concrete structures with potential mapping is limited due to the lack of accessibility, leading to high involvement of manpower and finally to high inspection costs. A main challenge in the coming decades will be the assessment of our ageing infrastructure and their repair. Automating corrosion assessments of structures by an inspection robot will increase the use of non-destructive test methods and the quality of assessments and consequently lead to a more profound basis for the decision making and planning of the maintenance of the ageing infrastructure and lower inspection costs. At ETH Zurich, the development of an omnidirectional flying inspection robot is currently being tackled as a collaborative effort between two research groups. The flying robot will collect the following data from the structure: (1) images of the surface, (2) potential of the steel and (3) electrical resistance between the sensor to the reinforcement. These measurements require the sensor to make physical contact with the concrete surface. As this contact task requires high stability and full 6 degree of freedom force and torque tracking to be robust in the field, no commercially available robot can be used. Preliminary flight tests with the electrochemical sensor mounted on the inspection robot on a laboratory sample demonstrate that potentials and resistances can be successfully measured, with results similar to measurements taken by hand

Spatial variability of half-cell potential data from a reinforced concrete structure—a geostatistical analysis

Corrosion in reinforced concrete structures is among the major degradation mechanisms. The quantification and description of the spatial distribution of the corrosion condition within a structure on the basis of condition assessments are important. This study considered half-cell potential mapping data as a widely used technique to detect corrosion in reinforced concrete structures. A four-step workflow was proposed to analyse half-cell potential data with geostatistical techniques, first consisting of trend identification and possible trend removal. The obtained residuals were then subjected to a quantile-quantile transformation. Subsequently, experimental variograms were calculated and fitted with variogram models to estimate the correlation lengths. A case study with data from a road tunnel confirms the applicability of the workflow. It was assumed that the identified trend is primarily a result of the heterogeneity of the exposure conditions within the structure that ranges several metres. The residuals are interpreted as the result of the heterogeneities of material resistances that give rise to spatial variability in corrosion probability on a shorter distance range. The proposed analysis may be utilised for service life modelling (e.g. based on random fields), planning maintenance works, or optimising the grid size for half-cell potential measurements.ISSN:1744-8980ISSN:1573-247

Potentials of Steel Fibres for Mesh Mould Elements

ISSN:2211-084