The construction sector significantly contributes to global greenhouse gas emissions, primarily from cement production. To mitigate this, a strategy focusing on reusing structural components from existing reinforced concrete structures is being explored. This study highlights challenges and presents initial results in designing new structures from reused elements. The objective is to develop methods for designing load-bearing structures using available elements from demolished buildings, categorized in a construction kit. The challenge is to find a structure that meets load-bearing capacity and architectural demands under the constraints of available elements. The feasibility of integrating existing foundations into the design process is investigated. Non-destructive measurements and simulations characterize the foundation and soil properties, while methods for strengthening or adjusting the foundation are developed. The design process considers the constraints of the foundation and construction kit, with the coordinated arrangement of reused elements and connection types controlling stress distribution. The structural reliability of the proposed structure is assessed, quantifying the effect of uncertainties related to individual elements. Modulare Strukturen aus wiederverwendeten Bauteilen: Herausforderungen bei der Nutzung von Bestandsgr & uuml;ndungenDie Bauwirtschaft tr & auml;gt durch die Zementproduktion erheblich zu den globalen Treibhausgasemissionen bei. Zur Reduktion soll eine Strategie zur Wiederverwendung von Stahlbetonbauteilen vorgestellt werden. In diesem Beitrag wird die Entwicklung von Methoden f & uuml;r den Tragwerksentwurf unter Verwendung verf & uuml;gbarer Bauteile, die aus abzurei ss enden Geb & auml;uden entnommen wurden und in einem Baukasten-System kategorisiert sind, pr & auml;sentiert. Die zentrale Herausforderung besteht darin, eine Struktur zu finden, die eine ausreichende Tragf & auml;higkeit aufweist und den architektonischen Anforderungen gen & uuml;gt. Hierbei sollen bestehende Fundamente in den Entwurfsprozess integriert werden. Dazu werden zerst & ouml;rungsfreie Messungen mit Simulationen kombiniert, um die Eigenschaften von Fundament und Boden zu charakterisieren. Weiterhin wird die M & ouml;glichkeit einer Verst & auml;rkung durch Biozementierung untersucht. Im optimierungsgesteuerten Entwurfsprozess werden die Fundamente und verf & uuml;gbaren Bauteile als Randbedingungen ber & uuml;cksichtigt, wobei der Kraftfluss durch die gezielte Anordnung der Bauteile und entsprechender Verbindungstypen gesteuert werden kann. Die Zuverl & auml;ssigkeit der abgeleiteten Tragstrukturen wird durch nichtlineare Simulationen bewertet und Unsch & auml;rfen werden bez & uuml;glich des Bauteilzustands quantifiziert.

A second-order nonsingular terminal sliding mode control (SONTSMC) is proposed to solve the stabilization and tracking problems of an inverted pendulum. Although, a first-order sliding mode controller with the integral of the cart position can eliminate the offset in the cart position caused by incorrect calibration of the pendulum angle while balancing the pendulum at the upright equilibrium position, its control precision and chattering reduction can be improved by using a higher-order sliding mode controller. Therefore, the SONTSMC is designed by combining nonsingular sliding mode control and first-order sliding mode control to construct a second-order sliding mode controller that enhances tracking accuracy and reduces the chattering problems associated with sliding mode control. The performance of the proposed control is compared with that of the linear quadratic regulator sliding mode control (LQRSMC) and the integral linear quadratic regulator sliding mode control (ILQRSMC) for CIP’s stabilization and tracking. The results indicate that SONTSMC significantly increases the control performance of CIP while efficiently utilizing control energy.

Although balancing of inverted pendulum is a well established problem, it continues to present more challenges in modern control designs. While sliding mode control has achieved considerable success in controlling inverted pendulums, most of these advancements have been primarily validated through simulations. Unfortunately, the complexity, and high computational demands of these controllers often limit practical implementation. To address these issues, we constructed a new inverted pendulum system, incorporating a stepper motor to reduce the backlash phenomenon commonly associated with a DC motor, thereby ensuring precise motion control. A simplified sliding mode controller was then designed for the augmented model of inverted pendulum and compared to a linear quadratic controller in practical implementation. The experimental results reveal that the sliding mode control significantly improves the system response, and effectively reduces the steady state errors in pendulum angle and cart position.

Stabilization and tracking problems for cart inverted pendulums under disturbances and uncertainties have posed significant challenges for control engineers. While various controllers have been designed for an inverted pendulum, they often overlook the calibration error of the pendulum angle in practical implementations, which degrades the control performance. Incorrect calibration of the pendulum angle in upright equilibrium position generates an offset of cart position errors. To solve this problem, an augmented model comprising integral cart position errors was first constructed. Afterwards, a sliding mode control was designed for this system based on a linear quadratic controller, to facilitate implementation. Additionally, a stepper motor was employed in the inverted pendulum to enhance the control performance and widen applicability in industrial settings. The effectiveness and performance of the proposed controller were validated by means of experimental studies, focusing on stabilization control and tracking control of a cart inverted pendulum actuated by a stepper motor.

Beim R & uuml;ckbau eines Geb & auml;udes verbleiben die Gr & uuml;ndungselemente oft im Boden, da ihre Entfernung und Wiederverwendung technisch und wirtschaftlich herausfordernd ist. Eine Wiederverwendung vor Ort bietet jedoch & ouml;kologische und & ouml;konomische Vorteile, da Ressourcen geschont, Emissionen reduziert und Bauzeiten verk & uuml;rzt werden. Voraussetzung hierf & uuml;r ist eine detaillierte Charakterisierung der Bestandsfundamente hinsichtlich ihrer strukturellen Integrit & auml;t, m & ouml;glicher Sch & auml;den und der verbleibenden Tragf & auml;higkeit. Einschr & auml;nkungen ergeben sich insbesondere durch die unver & auml;nderliche Bewehrungsanordnung, die die Lastaufnahme f & uuml;r neue Bauwerke begrenzt. Gleichzeitig kann eine Verst & auml;rkung oder Erg & auml;nzung der Bestandsfundamente erforderlich sein, was oft mit hohen Treibhausgas-Emissionen verbunden ist. Die Biozementierung mittels bakterieller Calciumcarbonat-Ausf & auml;llung bietet eine nachhaltige Alternative, deren mechanische Eigenschaften und Verbundwirkung mit Bestandsgr & uuml;ndungen jedoch noch erforscht werden m & uuml;ssen. Die Nutzung von Abwasser zur Biozementierung durch Ureolyse sowie der alternative Pfad der Denitrifikation sind im Hinblick auf die Reduktion der Kosten und eine Steigerung der Effizienz vielversprechend, jedoch bisher weitestgehend unerforscht. Dieser Beitrag beschreibt zwei Teilprojekte des SFB 1683 ,,Interaktionsmethoden zur modularen Wiederverwendung von Bestandstragwerken": B04, das sich mit der Charakterisierung von Gr & uuml;ndungen mittels inverser Identifikationsmethoden befasst, und A04, das bio-basierte Anpassungen von Bestandsgr & uuml;ndungen f & uuml;r eine nachhaltige Wiederverwendung untersucht. Beide Projekte sind Teil der Interaktionskette 1 ,,Zirkul & auml;re modulare Tragwerke aus wiederverwendeten Bauteilen" und leisten einen Beitrag zur Optimierung von Tragwerksplanung, Fundamentverst & auml;rkung und Zuverl & auml;ssigkeitsbewertung. Characterization and sustainable improvement of existing foundationsWhen deconstructing a building, foundation elements often remain in the ground, as their removal and reuse are technically and economically challenging. However, on-site reuse offers ecological and economic benefits. A prerequisite for this is a detailed characterization of the existing foundations in terms of their structural integrity, potential damage, and remaining load-bearing capacity. Limitations arise, particularly due to the immutable reinforcement arrangement, which restricts the load-bearing capacity for new structures. Strengthening or supplementing the existing foundations may be necessary, which is often associated with high greenhouse gas emissions. Bio-cementation through bacterial calcium carbonate precipitation offers a sustainable alternative, but its mechanical properties and bonding effect with existing foundations still need to be investigated. The use of wastewater for bio-cementation through ureolysis, as well as the alternative path of denitrification, are promising with regard to cost reduction and efficiency improvement, but have so far been largely unexplored. This paper describes two sub-projects of the SFB 1683: B04, which deals with the characterization of foundations using inverse identification methods, and A04, which investigates bio-based adaptations of existing foundations for sustainable reuse.

In robotics, reinforcement learning can train controllers or agents to find optimal solutions for complex tasks by enabling the robot to interact repeatedly with the environment. The reward function is an important aspect that guides any reinforcement algorithm to find the desired solution successfully. This work examines two deep reinforcement learning approaches, one uses a Deep Q-Network(DQN) and the other a deep deterministic policy gradient (DDPG) algorithm, applicable to navigation tasks for mobile robots. Comparison between different reward schemes for both algorithms is one of the main focuses in this work. The methodology is implemented in the simulation for a mobile robot called TurtleBot3. The task for the robot is to navigate through obstacles from an initial location to a goal position. Finally, the trained end-to-end navigation stack is also implemented on the actual TurtleBot3 in a real environment. The robot uses a Lidar sensor to detect obstacles. The Lidar measurements and the relative position and angle of the robot to the target location are the inputs to the controller. The TurtleBot3 also utilizes distance information from its Lidar sensor to create an environmental map using the simultaneous localization and mapping (SLAM) technique. Additionally, given an initial position, the robot employs its inertial measurement unit (IMU) sensor and encoders for precise localization.

Passive dynamic absorbers, also known as mass dampers, are typically investigated for their use in vibration suppression of buildings and structures. The proposed model consists of two rigid bodies, where the main body rests on a viscoelastic foundation. Additional attached mass represents a nonlinear energy sink. The dynamical interaction and behavior of a two-degree-of-freedom mechanical system are described with a system of two coupled differential equations of the second order. The first equation contains the damping term. The second equation is nonlinear. The nonlinear differential equation system is solved using an incremental harmonic balance method. The influence of the resting foundation's viscoelastic properties on the main body's dynamics is analyzed. Additionally, the variations of other system parameters, especially the mass damper features, on amplitude-frequency response curves are analyzed and discussed.

In this paper an integral linear quadratic regulator (ILQR) is proposed for balancing the inverted pendulum (IP) system. Dynamic model of the IP system, using the acceleration of the cart as control input is derived. Subsequently, the dynamic model is linearized at operation region to obtain a linear model for the controller design. This model is further enhanced by incorporating an integral term of the cart position to formulate augmented model for the ILQR control. The control input of the ILQR, essential for balancing the IP and keeping the cart at the desired position, is determined by solving the Riccati equation. Simulation and experimental results are presented to evaluate the effectiveness of the proposed control in balancing the IP while successfully reaching the desired cart position.

In light of the complex behavior of vibrating structures, their reliable modeling plays a crucial role in the analysis and system design for vibration control. In this paper, the reverse-path (RP) method is revisited, further developed, and applied to modeling a nonlinear system, particularly with respect to the identification of the frequency response function for a nominal underlying linear system and the determination of the structural nonlinearities. The present approach aims to overcome the requirement for measuring all nonlinear system states all the time during operation. Especially in large-scale systems, this might be a tedious task and often practically infeasible since it would require having individual sensors assigned for each state involved in the design process. In addition, the proper placement and simultaneous operation of a large number of transducers would represent further difficulty. To overcome those issues, we have proposed state estimation in light of the observability criteria, which significantly reduces the number of required sensor elements. To this end, relying on the optimal sensor placement problem, the state estimation process reduces to the solution of Kalman filtering. On this ground, the problem of nonlinear system identification for large-scale systems can be addressed using the observer-based conditioned RP method (OBCRP) proposed in this paper. In contrast to the classical RP method, the current one can potentially handle local and distributed nonlinearities. Moreover, in addition to the state estimation and in comparison to the orthogonal RP method, a new frequency-dependent weighting is introduced in this paper, which results in superior nonlinear system identification performances. Implementation of the method is demonstrated on a multi-degree-of-freedom discretized lumped mass system, representing a substitute model of a physical counterpart used for the identification of the model parameters.

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures.

Unmanned ground vehicles (UGVs) have gained increased attention in different fields of application; therefore, their optimization requires special attention. Lowering the mass of a UGV is especially important to increase its autonomy, agility, and payload capacity and to reduce dynamic forces. This contribution deals with optimizing a UGV unit prototype that, when connected with similar units, forms a moving electric fence for animal grazing. Together, these units form a robotic system that is intended to solve the critical problem of lack of human capacity in herding and grazing. This approach employs topology optimization (TO) and finite element analysis (FEA) to lower the mass of a UGV unit and validate the design of its structural components. To our knowledge, no optimization of this type of UGV has been reported in the literature. Here, we present the results of a case study in which a set of four load cases served as a basis for the optimization of the UGV frame. Response surface analysis (RSA) was used to identify the worst load cases, while substructuring was used to allow for more detailed meshing of the frame portion that was subjected to TO. Thereby, we demonstrate that the prototype of the UGV unit can be built using standard parts and that TO and FEA can be efficiently used to optimize the load-carrying structure of such a specific vehicle.

AbstractEffective exploration techniques during mechanized tunneling are of high importance in order to prevent severe surface settlements as well as a damage of the tunnel boring machine, which in turn would lead to additional costs and a standstill in the construction process. A seismic methodology called full waveform inversion can bring a considerable improvement compared to state-of-the-art seismic methods in terms of precision. Another method of exploration during mechanized tunneling is to continuously monitor subsurface behavior and then use this data to identify disturbances through pattern recognition and machine learning techniques. Various probabilistic methods for conducting system identification and proposing an appropriate monitoring plan are developed in this regard. Furthermore, ground conditions can be determined by studying boring machine data collected during the excavation. The active and passive obtained data during performance of a shield driven machine were used to estimate soil parameters. The monitoring campaign can be extended to include above-ground structural surveillance as well as terrestrial and satellite data to track displacements of existing infrastructure caused by tunneling. The available radar data for the Wehrhahn-line project are displayed and were utilized to precisely monitor the process of anticipated uplift by injections and any subsequent ground building settlements.

This article aims to develop a new Adaptive Proportional Integral Derivative (PID) Nonsingular Dual Terminal Sliding Mode Control, designed for tracking the position of robot manipulators under disturbances and uncertainties. Compared with existing PID Nonsingular Fast Terminal Sliding Mode (PIDNFTSM) controllers, this work effectively avoids singularity problems in control while significantly enhancing the convergence speed of errors. An adaptive reaching law is proposed to estimate the bound information of the first derivative of lumped disturbance by regulating itself based on sliding variables. The overall system stability is proven by using the Lyapunov approach. Subsequent simulation results verify the effectiveness of the proposed controller regarding tracking error reduction, energy efficiency enhancements, and singularity avoidance.

This work aims to improve the classical white-box model of an inverted pendulum in order to reach a more accurate representation of an actual pendulum on a cart system. The purpose of the model is to train different controllers based on machine learning algorithms. In the context of this paper, the inverted pendulum system is driven by a belt drive that is controlled by a stepper motor. Due to the nature of the controller, the input to the stepper motor is in the form of a non-smooth bang-bang-like signal that moves the cart to the left, right, or terminates its movement. One of the main challenges, in this case, is to find a proper function to model the stepper motor as its dynamics cannot be captured with a constant gain. It has been shown that the transient behavior of the stepper motor when changing direction or stopping is not negligible in the closed-loop control performance. Accordingly, a grey-box scheme, which accounts for the uncertainties that are not included in the vanilla white-box model, is utilized to achieve a lower model mismatch compared to the actual pendulum. Initially, the equation of motion was derived using the Euler-Lagrange equation with force on the cart as the control input. But in the real-time experiment, the interface is realized by the stepper motor's frequency modulator, hence a transfer function representing the relationship between the frequency and the force applied on the cart (in the model) is calculated as a black-box model. To improve the accuracy of the transfer function, an experimental data-driven design of this function is performed based on modern schemes in system identification. For this purpose, the applied frequency to the stepper motor and the states from the actual system are recorded. Then, the applied force on the cart is calculated using the equation of motion and the recorded states. It is also shown that the frequency-force transfer function uncertainty due to exogenous disturbances is non-negligible and with the aim of having a more accurate model, an artificial neural network is introduced. Finally, the effectiveness of this grey-box model is shown by training and implementing a deep Q-network based controller to swing up and balance the inverted pendulum.

Background Structural damage can be caused by various factors such as aging, environmental conditions, and unexpected events like earthquakes. Early detection of damage is crucial to prevent further deterioration, avoid catastrophic failure, and reduce maintenance costs. Damage detection methods that use piezoelectric sensors have gained popularity due to their non-destructive and non-invasive nature. Despite the progress made in the field of damage detection using piezoelectric sensors, there is still a need to improve the accuracy and reliability of those methods. Objective This study aims to contribute to this by investigating the damage detection hybrid method, which uses the time-of-flight (ToF) criteria of acquired signals besides the energy loss damage index (DI) between damaged and intact states of a specimen, and exploring its possible improvements. The improvement potential in the investigated method regarding the signal processing details and the specification of the ToF used within the method, where the lack of information has been identified. Thus, the present study concentrates on those factors to get more benefit of the suggested method and extend its applicability. Results The investigated factors play significant role in the accuracy and reliability of the method. By analyzing these criteria, this study contributes to the development of more advanced and reliable damage detection methods that can be applied to a wide range of structures, improving the ability to assess their structural health and safety. This study provides a better understanding of the hybrid method and contributes to the development of more accurate and reliable damage detection methods. The results of this study indicate that the proposed hybrid method effectively detects damage in the structural components under investigation with high accuracy and reliability. Methods A 2D concrete plate is utilized to apply the proposed methodology. Hereby, various ToF criteria, truncation strategies of the signals, and the number of piezoelectric transducers used in the numerical experiment are examined to investigate their impact on the damage detection accuracy. Conclusion Performance of the method was found to be significantly affected by selection of the investigated parameters, as well as of the number and placement of sensors. The findings suggest that a thorough analysis of these criteria can lead to further improvements in the accuracy and reliability of damage detection methods.

This paper aims to develop a novel hierarchical recursive nonsingular terminal sliding mode controller (HRNTSMC), which is designed to stabilize the inverted pendulum (IP). In contrast to existing hierarchical sliding mode controllers (HSMC), the HRNTSMC significantly reduces the chattering problem in control input and improves the convergence speed of errors. In the HRNTSMC design, the IP system is first decoupled into pendulum and cart subsystems. Subsequently, a recursive nonsingular terminal sliding mode controller (RNTSMC) surface is devised for each subsystem to enhance the error convergence rate and attenuate chattering effects. Following this design, the HRNTSMC surface is constructed by the linear combination of the RNTSMC surfaces. Ultimately, the control law of the HRNTSMC is synthesized using the Lyapunov theorem to ensure that the system states converge to zero within a finite time. By invoking disturbances estimation, a linear extended state observer (LESO) is developed for the IP system. To validate the effectiveness, simulation results, including comparison with a conventional hierarchical sliding mode control (CHSMC) and a hierarchical nonsingular terminal sliding mode control (HNTSMC) are presented. These results clearly showcase the excellent performance of this approach, which is characterized by its strong robustness, fast convergence, high tracking accuracy, and reduced chattering in control input.

Early damage detection in structures and systems plays a significant role in their overall life cycle since it enables early action towards preventing serious failure.The methods of structural health monitoring (SHM) provide reliable tools for early damage detection.Having information about the intact structure, SHM methods are developed that rely on discrepancy between the characteristics of the pristine and damaged structure.In this work particularly the structural elements constituting civil structures are considered from the point of view of further development, analysis, improvement, and implementation of the methods for damage detection in concrete and concrete-like materials.The methods addressed are based on the propagation of guided ultrasound waves through the structural elements under investigation, initiated by a piezoelectric driven source.Damage indices are defined based on the propagating wave characteristics, taking into consideration weakening of the wave energy in the presence of a damage.In addition, the methods are hybridized considering at the same time the signal time of flight in combination with interpolation of the damage index values over the discrete domains.Indication of the damage presence and its location is obtained through a corresponding superposition of the damage indices depending on the damage location, as well as on the number of excitation/receiver points on the considered structure.For two-dimensional propagation different study cases are considered in order to investigate detectability of the damage in dependence of the number of actuator/sensors points and their location.The feasibility of the approach is demonstrated in numerical experiment, whereas the required waveforms can be acquired in a similar manner from real experiments.

The state space representation of linear and nonlinear systems is widely used in the literature for system characterization, system identification, and model‐based control synthesis. In the case of systems with local nonlinearity, the realization of a state space model where the states are physically interpretable as a requirement has been a challenging task. This requirement becomes more emphasized, especially in the scope of industrial high‐precision systems. Consequently, the class of black‐box system identification approaches becomes less attractive. In the scope of this paper, we are interested in modeling systems with dominant local nonlinearity where employing linear models can only cover a limited range of system dynamics. More specifically, geometric nonlinearities which are present in the joints (bolted) of structural interfaces are analyzed. The main goal is to provide systematical modeling of such systems without using a sparse nonlinear representation. Such a low‐order nonlinear model can improve the simplicity of analyzing the nonlinear system and can be used for structural vibration and noise control. In order not to neglect the sophisticated linear modeling technique, the linear model is proposed to be extended by means of smooth nonlinear terms. The systematic approach contains the modeling of the linear counterpart followed by the localization and characterization steps in the well‐known three‐step paradigm. For the characterization step, this work relies on the acceleration surface method (ASM). The experimental setup under study, as a benchmark, is a set of two beams of different lengths and thicknesses connected by a screw that is excited by a mechanical shaker. The axes are oriented in the transverse direction of the beams, while the boundaries are realized as imperfect clamped‐clamped boundary conditions at two ends in the model. Consequently, by selecting the excitation amplitude, we can control the dominant dynamics at lower excitation amplitudes and invoke the local nonlinearity at higher amplitudes. For higher excitation levels using sine sweep signals, the phase space information of the shaker and accelerometer sensors is used to detect the local nonlinearities along the clamped‐clamped beam. The detected and characterized nonlinearities are incorporated into the linear system as a systematic approach for modeling such a structurally nonlinear system.

The application of effective exploration techniques in mechanized tunneling is crucial in order to obtain a knowledge of the subsoil prior to drilling. We present a three-staged method, which seeks to image the excavation environment in detail, also reducing the computational demand of common exploration techniques. The algorithm combines two approaches: supervised machine learning and full waveform inversion. Firstly, the machine learning algorithm is applied on data sets of measured pore water pressures and ground settlements during tunnel propagation, making a primary prediction of geological changes ahead of the boring machine. Secondly, seismic measurements are acquired for full waveform inversion based on parameter identification. This method incorporates the primary predictions from the supervised machine learning in the form of a parametrization of the position, shape and material properties of the disturbance. Thirdly, the subsurface model gained out of the second stage is utilized as a starting model for a second full waveform inversion using the adjoint method, providing an even more detailed image of the subsurface. The exploration algorithm is tested on a synthetic shallow tunnel environment with an unknown obstacle ahead of the tunnel boring machine. It is shown that the algorithm finds the unknown obstacle with improved accuracy.

Identification of damage in its early stage can have a great contribution in decreasing the maintenance costs and prolonging the life of valuable structures. Although conventional damage detection techniques have a mature background, their widespread application in industrial practice is still missing. In recent years the application of Machine Learning (ML) algorithms have been more and more exploited in structural health monitoring systems (SHM). Because of the superior capabilities of ML approaches in recognizing and classifying available patterns in a dataset, they have demonstrated a significant improvement in traditional damage identification algorithms. This review study focuses on the use of machine learning (ML) approaches in Ultrasonic Guided Wave (UGW)-based SHM, in which a structure is continually monitored using permanent sensors. Accordingly, multiple steps required for performing damage detection through UGWs are stated. Moreover, it is outlined that the employment of ML techniques for UGW-based damage detection can be subtended into two main phases: (1) extracting features from the data set, and reducing the dimension of the data space, (2) processing the patterns for revealing patterns, and classification of instances. With this regard, the most frequent techniques for the realization of those two phases are elaborated. This study shows the great potential of ML algorithms to assist and enhance UGW-based damage detection algorithms.