Climate-Neutral and Resource-Efficient Construction

• Systematic tailored investigation of practical applications of bridge digital twins. • Structured classification and critical analysis of data technologies for bridge digital twins • Innovative pilot application of a bridge digital twin in Germany, showcasing cutting-edge practice • Transforming bridge lifecycle management of the historical Nibelungen Bridge Digital Methodologies, particularly digital twin technology, have the potential to enable data-driven design, construction, operation, maintenance, and demolition of bridges, fostering a fundamental digital transformation of their entire life cycle management. To comprehensively explore its potential, this work presents a two-part study comprising a state-of-the-art review of digital twin applications in bridge engineering and a pilot case study. In the first part, a systematic investigation of scientific publications on bridge digital twins is conducted. Initially, relevant data are systematically collected and analyzed. This is followed by an elaboration of general definitions, classifications, and modeling approaches related to bridge digital twins. Subsequently, key data technologies relevant to digital twin applications, including data acquisition, transmission, and integration, are examined in detail. In the second part, the digital twin of the Nibelungen Bridge in Germany, developed using cutting-edge, market-available technologies, is comprehensively presented. Finally, the study concludes with a discussion and an outlook on future developments.

potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules-for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Using the DFTBEPHY approach, carrier mobilities in graphynes are evaluated through CRTA, SERTA, and analytical models, highlighting band non-parabolicity and its impact on carrier transport.

ABSTRACT The rapidly growing demand for sensors in the Internet of Things era calls for magnetic sensors that simultaneously deliver high performance, operational versatility, and environmental sustainability. However, simultaneously achieving these goals remains challenging. This work reports a unified material and fabrication strategy that addresses these issues through a green printing and recycling paradigm. By employing a solvent‐orthogonal binder design, i.e., utilizing water‐soluble polyvinyl alcohol for sensing elements and solvent‐soluble poly(methyl methacrylate) for electrodes, we enable high‐fidelity multi‐layer deposition with robust interlayer structural integrity and zero cross‐dissolution, as well as a mild, rapid disassembly process for full material reclaim. The sensors' operational regimes are precisely tailored by varying the dimensionality and magnetic anisotropy of functional fillers. Permalloy microparticle‐based sensors utilize the anisotropic magnetoresistance effect to achieve high sensitivity in low‐field regimes (< 3.5 mT), while Co/Cu nanoflakes and CoNi nanowires leverage giant magnetoresistance and extreme shape anisotropy to extend the sensing range to 65 mT and 420 mT, respectively. Furthermore, our devices exhibit robust environmental stability under water immersion and thermal stress, alongside exceptional endurance over 5,000 magnetization cycles. Finally, the practical utility of this platform is demonstrated through wearable interactive switches and smart‐home motion‐tracking systems.

Abstract In diesem Beitrag wird ein multimodaler, automatisierter Fertigungsansatz für ein modulares, materialeffizientes CFK‐bewehrtes Betondeckensystem vorgestellt. Das vorgeschlagene Konzept kombiniert einzelne robotergestützte Fertigungsprozesse – 3D‐Betondruck, robotisches Gießen von Beton, automatisierte Bewehrungsintegration und robotisches Fräsen – zu einem durchgängigen digitalen Prozess. Die einzelnen Deckenmodule bestehen aus dünnen Platten und einer gerippten, lastangepasst‐tragenden Unterkonstruktion und werden einzeln gefertigt. Die Module werden über lösbare Trockenverbindungen durch externe Vorspannung assembliert. Experimentelle Untersuchungen zum Verbundverhalten, zur Biege‐, Querkraft‐ und Durchstanztragfähigkeit sowie zur Lastübertragung in der Trockenverbindung bestätigen die Machbarkeit des Systems und zeigen den Einfluss von Bewehrungskonfiguration und Fertigungsparametern auf das mechanische Verhalten. Die Ergebnisse zeigen, dass der multimodale Ansatz die Herstellung tragfähiger Module ermöglicht und zugleich den Materialverbrauch gegenüber konventionellen Decken signifikant reduziert. Damit trägt die vorgestellte Methodik zur Entwicklung skalierbarer Strategien für die industrielle Fertigung adaptiver und ressourceneffizienter gerippter Betonbauteile bei.

The construction sector accounts for a substantial share of global greenhouse gas emissions, making effective strategies for the reuse and recycling of building materials indispensable. However, relevant information may be lost over the relatively long use phase of buildings. Digital Product Passports (DPPs) offer a standardized means of preserving and communicating product information across the life cycle and may therefore also be applied in the construction sector to facilitate reuse and recycling of building components, even after service lives exceeding 50 years. This paper presents the results of a first, interdisciplinary study that (i) develops a DPP for concrete elements using the Asset Administration Shell (AAS) and (ii) experimentally evaluates how DPP-presented information shapes consumer perceptions of recycled aggregate concrete (RAC) versus natural aggregate concrete (NAC) stair elements. In a scenario-based vignette experiment (N = 83), participants evaluated eight DPP mock-ups in which material (RAC vs. NAC), environmental impact (low vs. high), and structural performance (high vs. low) were systematically manipulated. Participants indicated their willingness to pay, perceived environmental value, perceived functional risk and product preference for each DPP. Repeated-measures ANOVAs showed robust main effects of material and environmental impact on perceived environmental value, and main effects of material and structural performance on perceived functional risk. Willingness to pay and product preference were higher for RAC than NAC, for low versus high environmental impact, and for high versus low structural performance. Overall, RAC was perceived as more environmentally valuable but also as riskier than NAC, even when objective environmental and structural indicators were held constant. The results indicate that DPP design should account for target-group-specific interpretation and potential biases in processing technical and sustainability information, to better support resource-efficient decision-making in the construction sector.

ABSTRACT Non‐invasive lactate monitoring in saliva holds considerable potential for disease diagnostics and personalized oral healthcare. However, the complex salivary matrix, characterized by high protein content, pellicle formation, and elevated viscosity, poses major challenges for electrochemical biosensors, including biofouling, signal interference, and limited stability. Here, we present an electrochemical lactate biosensor rationally engineered for reliable operation in untreated human saliva. A multilayer surface architecture comprising Prussian Blue, lactate oxidase, bovine serum albumin (BSA), and a zwitterionic polydopamine–MPC (pDA/MPC) coating minimizes nonspecific adsorption while preserving substrate accessibility. The optimized sensor exhibits a sensitivity of −0.48 µA/ mM − 1 and a linear range up to 2.5 mM in phosphate buffer at an operating potential of +0.055 V. In untreated human saliva, the sensor accurately quantified endogenous lactate and detected up to an additional 1 mM lactate before reaching saturation, covering the physiologically relevant concentration range. The multilayer architecture further provided stable and reproducible responses with negligible interference from glucose, uric acid, and ascorbic acid. Cytotoxicity studies using human gingival fibroblasts confirmed the biocompatibility of the modified sensor surface. These results demonstrate that rational multilayer surface engineering enables robust electrochemical lactate sensing in saliva and represents a promising strategy for future intraoral biosensing applications.

Abstract Hydrogen sulfide (H 2 S) is a highly toxic and corrosive gas that requires continuous, real-time monitoring at ultra-low concentrations in complex environments. Here we report a wireless, ultra-low-power H 2 S sensing platform for long-term, on-site detection. The device achieves a response exceeding 10,000%, a calculated detection limit of 0.328 ppb, and a 7 s response at room temperature, while consuming less than 0.7 μW. This performance represents a 4–5-fold faster response and up to 10 5 -fold lower active sensing power than commercial H 2 S sensors. The Au-interfaced MOF-derived architecture synergistically modulates Schottky barriers and grain boundaries, producing a 0.4 eV Schottky barrier shift and increasing current density by more than 10 6 -fold. We further demonstrate practical monitoring of food spoilage, pipeline leakage, and H 2 S accumulation in confined industrial environments, highlighting a robust strategy for next-generation smart environmental monitoring.

Abstrakt Für den breiten Einsatz von Carbonbeton in der Baupraxis sind Fügetechniken zum kraftschlüssigen Verbund vorgefertigter Bauteile erforderlich. Neben mechanischen Verbindungsmitteln, wie Dübel und Bolzen, kommen hierfür vor allem Epoxidharzklebstoffe und mineralische Mörtelklebstoffe zum Einsatz, wobei letztere eine nachhaltigere Alternative darstellen. Eine zentrale Herausforderung aller Systeme liegt im zerstörungsfreien Lösen der Verbindungen am Ende der Nutzungsdauer. Der vorliegende Beitrag greift diese Problematik auf und untersucht den Einsatz alternativer Zemente als schaltbare Klebstoffe für Carbonbetonbauteile. Die Korrosionsbeständigkeit der Carbonbewehrung ermöglicht in diesem Kontext neue Materialkombinationen, die im herkömmlichen Stahlbeton nicht praktikabel sind. Erstmals wird Sorelzement, ein chloridhaltiger Magnesiazement, als schnell erhärtender mineralischer Klebstoff für Carbonbeton untersucht. Darüber hinaus wird dessen selektive Auflösung in der Klebefuge durch ein saures Milieu erprobt – nach dem Prinzip „Debonding on Demand“. Dieser Beitrag beschreibt den Entwurf eines Klebstoffs auf Basis von Sorelzement hinsichtlich Verarbeitbarkeit, Erhärtungszeit und Lösbarkeit. Die Leistungsfähigkeit wird durch Haftzugversuche validiert und abschließend werden mit Sorelzement vergossene Vollstöße zwischen Carbonbetonfertigteilen im großformatigen Vier‐Punkt‐Biegeversuch getestet, um die Übertragung auf praxisrelevante Bauteilabmessungen zu erproben.

The integration of reinforcement remains a key challenge in 3D concrete printing (3DCP) and continues to limit its structural application. Existing approaches are largely based on metallic reinforcement or discrete fibers and are not fully compatible with the geometric flexibility and process characteristics of additive manufacturing. Textile reinforcement, consisting of continuous fiber-based yarns arranged in two- or three-dimensional architectures, offers a promising alternative due to its flexibility, high tensile strength, and corrosion resistance. This paper presents a comprehensive review and proposes a process-based classification framework for the integration of textile reinforcement into 3DCP. Building on the RILEM taxonomy for digital fabrication with concrete, integration strategies are systematically categorized according to their position within the manufacturing sequence, distinguishing between single-step processes during concrete shaping and two-step processes before or after shaping. The framework is used to analyze existing approaches and to relate them to reinforcement configurations, bond mechanisms, and resulting mechanical performance. Particular attention is given to testing methodologies and the specific challenges associated with additively manufactured textile-reinforced concrete. The study highlights key limitations, particularly with respect to bond, anisotropy, and process constraints, and provides guidance for the development of structurally efficient and scalable 3D-printed systems.

Abstract Alkali-activated materials (AAMs) have emerged as a promising alternative to traditional cement-based binders. However, their adoption is restricted by non-user-friendly production processes, the need for curing at elevated temperatures when higher amounts of aluminosilicate sources are used and the limited availability or regional dependency of precursors and activators. This study focuses on the development of one-part AAMs made with low-kaolinitic calcined clays as the main precursor (between 63–100%wt.) cured at ambient temperature. A comprehensive assessment of reaction kinetics, rheology and mechanical/microstructural properties was conducted. Results demonstrate promising fresh and hardened properties with an optimal AAM formulation including 81% of calcined clay achieving a flow diameter of 17 cm, initial/final setting times ranging from 50 to 116 minutes and 28-day compressive strength of approximately 40 MPa. Low-volume slag governs hydration kinetics and microstructure by supplying calcium-rich species and nucleation sites that activate ambient-temperature reactions and promote hybrid gel formation. It modulates setting time and refine porosity toward meso scales. Alongside an optimized solid-silicate regime, this slag-limited formulation optimizes ambient-cured fresh and hardened properties yielding a site-ready binder for multi-purpose applications. The study represents an attractive opportunity for a broader use of low-grade calcined clays as a practical and sustainable alternative to Portland-cement-based binders.

Abstract Construction workflows remain fragmented across design, robotic fabrication, and heavy machine operation, with each domain relying on separate interfaces that limit integration and operator accessibility. This paper proposes Extended Reality (XR) as a unifying interaction layer that bridges these domains by combining spatial augmentation, real-time telemetry, and an architecture designed to incorporate IFC-based construction data into a coherent operator interface. We present two prototypes involving a precise robot and heavy machinery. User studies demonstrate that XR-based interfaces improve spatial understanding and lower the entry barrier for inexperienced operators compared to conventional controls. We further identify latency, calibration stability, and haptic feedback as the key technical boundaries that currently constrain XR's effectiveness in these settings, providing a concrete design agenda for future development. Together, the prototypes demonstrate that a shared XR layer can meaningfully connect robotic fabrication and heavy machine teleoperation, offering a foundation for more integrated human–machine workflows in construction automation.

Bridge design is a complex challenge influenced by topography, geotechnical conditions, and construction methods, with the elevation profile being crucial for determining structural typology and foundation design. This paper presents a method for automatically extracting bridge elevation profiles from a limited set of images, addressing data scarcity and semantic interpretation challenges. The workflow includes: (1) semantic segmentation of structural components, (2) image-based 3D scene reconstruction, (3) point cloud alignment and scaling, and (4) elevation profile extraction. Extracted profiles are evaluated against high-resolution 3D laser scans, achieving a mean cloud-to-cloud distance as low as 21 cm using only 12 images. Comparisons with public GIS data show superior accuracy for small bridges and, in an initial test, comparable results for larger structures. This approach enables efficient acquisition of topographical data from any available imagery, including public sources, reducing reliance on traditional surveying and potentially expediting future bridge design processes.

Abstract The construction sector faces increasing pressure to reduce greenhouse gas emissions and mitigate its substantial contribution to global waste streams, with demolition alone generating a major share within the EU. Controlled deconstruction in sense of recovering components non-destructively for reuse offers a viable alternative to conventional demolition but remains labor-intensive and potentially hazardous. This paper investigates how on-site robotics can enable automated, safe, and material-preserving deconstruction through a feedback loop in a design-to-process flow. Building on robot-oriented de-/construction research, we develop and implement a robotic unbolting workflow tailored to reclaim beams from a modular steel demonstrator. The system integrates multi-sensor feedback to localize, align with, and remove standardized bolts under realistic construction site conditions. Iterative field trials demonstrate that the robot can repeatedly execute non-destructive unbolting without manual intervention, reliably recovering connection members while keeping workers at a safe distance. The results highlight the feasibility of robotic, controlled deconstruction and point toward future integration of sensing-informed autonomy within circular construction ecosystems.

Magnesium oxychloride (MOC) cement is gaining renewed attention as a low-carbon construction material. However, conventional MgO production is constrained by the geographically limited availability of MgCO3 and its energy-intensive calcination at temperatures up to 1000 °C. Thermal recycling of waste MOC has so far been demonstrated in the 600–1000 °C range, leaving the lower temperature limits underexplored. This study investigates medium-temperature MOC recycling at 400–700 °C and assesses its feasibility by fully replacing virgin MgO with recycled magnesia powder (RMP). Results show incomplete decomposition and residual chlorides below 500 °C, the highest reactivity at 550–600 °C, where the active MgOα content exceeded that of the precursor, and a sharp decline in reactivity above 600 °C. Rehydrated MOC (R_MOC) retained up to 90% of the 28-day compressive strength of the reference mixture. Residual chlorides promoted Chlorartinite (Mg₂(CO₃)Cl(OH)·3H₂O) formation and favored Phase 3 (3Mg(OH)2·MgCl2·8H2O) over Phase 5 (5Mg(OH)2·MgCl2·8H2O). These findings define the lower temperature bound for energy-efficient MOC recycling and support the fully circular use of MOC waste.

Context-awareness is crucial for developing cyber-physical systems to enable dynamic behavior. Context-oriented programming (COP) aims to improve the definition of context-dependent behavioral variations. However, existing layer-based COP approaches have two major limitations: layered methods are specified only for single contextual dimensions and restrict adaptations to method replacement or input/output filtering, limiting expressiveness for complex context dependencies.

ABSTRACT Two‐dimensional conjugated metal–organic frameworks (2D c ‐MOFs) offer an appealing platform for electronic devices, particularly chemiresistive sensors, owing to their unique combination of electrical conductivity and intrinsic porosity. However, their pronounced structural and transport anisotropies render device performance highly sensitive to layer orientation, underscoring the need for synthetic strategies that enable the controlled synthesis of well‐aligned 2D c ‐MOF films. Here, we introduce a surfactant monolayer‐assisted on‑water synthesis that programs the layer orientation of conductive Ni‑HHTP (HHTP = 2,3,6,7,10,11‐hexahydroxytriphenylene) films (face‑on vs. edge‑on) over cm 2 ‑scale areas by tuning ligand‐surfactant monolayer electrostatic vs. hydrogen‑bonding interactions. Imaging and scattering techniques unambiguously confirm preferential face‐on and edge‐on layer orientations, while electrical transport and optical pump‐THz probe spectroscopy reveal markedly enhanced intralayer charge transport in face‐on films, motivating their integration into chemiresistive sensing. Chemiresistive NH 3 sensors based on face‑on Ni‑HHTP films achieve a response of 269.8% at 50 ppm and an ultralow detection limit of 8.45 ppb at room temperature, surpassing edge‑on films and previously reported 2D c ‑MOF sensors. These results establish surfactant‐programmed on‐water synthesis as a new route to macroscopic orientation control in 2D c ‐MOFs, enabling deliberate exploitation of their anisotropic charge transport in high‐performance sensing and electronic devices.

Abstract This project investigates a novel approach to the continuous production of reinforced concrete modules of varying geometries for the assembly of shell structures. To this end, digital design and automated manufacturing methods were employed, enabling the decoupling of geometric complexity from production time and cost. In the first project phase, flat modules were developed by discretizing free‐form shells into unique planar quadrilateral elements, forming grid‐shell‐like structures. Algorithms for discrete geometric representation, faceting, and parametric form‐finding were created and integrated into a continuous digital workflow linking design and fabrication. In parallel, a fully automated production process was established using advanced technologies such as 3D concrete printing and robotic reinforcement placement. The second project phase focused on advancing the technology toward industrial applicability. This included the production of curved modules, the development of new shell segmentation strategies, the implementation of post‐processing techniques to improve geometric accuracy, and the introduction of more sustainable materials and automated quality control methods.

Abstract This paper presents the development of a multimodal, automated fabrication approach for a modular, material‐efficient reinforced concrete slab system. The proposed concept combines 3D concrete printing (3DCP), robotic casting, and the automated integration of tailored preformed carbon fiber‐reinforced polymer (CFRP) reinforcement profiles. The individual slab modules, consisting of thin plates and ribbed substructures, are manufactured in a continuous, digitally controlled process and assembled using demountable dry joint connections with external post‐tensioning. To achieve the smooth and geometrically precise surfaces required for these connections, the modules are post‐processed by robotic milling. Experimental investigations on bond, bending, shear, punching, and the load transfer in the dry joint connection demonstrate the structural feasibility of the system and highlight the influence of reinforcement configuration and fabrication parameters. The results show that the multimodal approach enables the production of modules with high load‐bearing capacity while significantly reducing material usage compared to conventional slab systems. The presented methodology contributes to the development of scalable approaches for the industrial production of adaptive and resource‐efficient ribbed reinforced concrete members.

Computational design of sensing materials remains fundamentally challenging due to the vast configurational landscape and the absence of robust property correlations that enable efficient molecular representation. These challenges become particularly critical in healthcare applications, where reliable and interpretable molecular recognition is essential for non-invasive diagnostics. To address these challenges, we developed MORE-ML, a quantum-informed AI framework that combines electronic-structure-derived properties of e-nose molecular building blocks with machine learning (ML) methods to uncover sensing mechanisms and guide the design of new systems. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between body odor volatilomes (BOV) molecules and mucin-derived receptors, both in the gas phase and when deposited on graphene. MORE-QX provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of the property space revealed weak correlations between quantum-mechanical (QM) properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Through explainable AI, we reduced the high-dimensional QM property space to a compact and physically interpretable set of descriptors, revealing the properties that most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for artificial sensing materials in BOV sensing. This approach establishes a foundation for advancing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.