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.

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.

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.

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 RNA is a flexible biopolymer that adopts diverse conformations while forming structural motifs essential for its function. Classical RNA force fields often show limited transferability and inefficient sampling of transitions between stable states, particularly in moderately large RNA. To address these limitations, quantum-informed machine learning (ML) potentials have recently emerged as a promising alternative, offering improved accuracy and transferability relative to classical force fields. Here, we assess ML potentials for exploring RNA conformations using the adenine–adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block. We generated an extensive quantum-mechanical (QM) dataset for ApA conformations obtained from temperature replica exchange molecular dynamics (TREMD) simulations. Despite its small size, the ApA dimer exhibits six conformations in which quantum effects and solvent-mediated interactions play crucial roles. Using this dataset, we parameterized ML potentials based on the equivariant MACE architecture and informed by both ab-initio and semi-empirical data. The resulting potentials reproduce key conformational features of the ApA system, including base stacking, sugar puckering, and backbone flexibility, and provide broader coverage of structural transitions than the general-purpose SO3LR and MACE-OFF24 models. These findings highlight the importance of quantum-accurate RNA force fields towards the structural and energetic characterization of RNA complexes.

In cement-based matrices, polyethylene terephthalate (PET) fibers are prone to alkaline hydrolysis, limiting their structural applicability in strain-hardening cementitious composites (SHCC). This study evaluates two mitigation strategies: (i) reducing matrix alkalinity through clinker substitution and (ii) applying enforced carbonation curing. Three limestone calcined clay cement (LC 3 ) matrices containing 50%, 35%, and 25% clinker by weight were prepared to generate systems with decreasing alkalinity levels. PET-reinforced LC 3 composites were subjected to accelerated steam-curing aging (40 °C, 100% RH) for 14, 28, 60, and 90 days to assess fiber degradation, fiber-matrix bond performance via single-fiber pull-out tests, and strain-hardening behavior via uniaxial tension tests. A subset of specimens additionally underwent enforced carbonation curing (20% CO 2 , 70% RH, 24 h) before steam curing to evaluate its effectiveness in preserving fiber integrity. Results revealed a strong dependence of PET stability on matrix composition: severe degradation occurred in LC 3 -50, moderate in LC 3 -35, and minimal in LC 3 -25. Enforced carbonation effectively mitigated degradation across all matrices, with particularly pronounced benefits in higher-alkalinity systems (LC 3 -50 and LC 3 -35). In LC 3 -25 composites, enforced carbonation was unnecessary and even detrimental, impairing matrix integrity, fiber-matrix bond strength, and strain-hardening performance. These findings suggest that approaches to mitigate fiber degradation must be tailored to the matrix composition. While enforced carbonation is crucial for high-clinker SHCC, where fibers are most at risk, it can be counterproductive in low-clinker systems. Overall, this research offers valuable insights for designing durable, sustainable SHCC reinforced with PET fibers. • High-clinker matrices accelerate the degradation of PET fibers in SHCC. • Low-clinker matrices effectively limit PET fiber degradation. • Enforced carbonation mitigates PET fiber degradation in SHCC. • In low-clinker matrices, enforced carbonation reduces mechanical properties.

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.

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.

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.

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.

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 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 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.

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.

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.

Industry reports document a surge in cement production in recent years, reaching 4.1 billion tonnes in 2022 and resulting in significant environmental burdens. While conventional supplementary cementitious materials often fail to meet construction demands, limestone calcined clay cements (LC 3 ) offer a promising alternative, reducing reliance on traditional raw materials while using abundant resources. This study evaluates the sustainability potential of strain-hardening cementitious composites based on LC 3 binders incorporating dispersed non-metallic synthetic fibers. A cradle-to-gate life cycle assessment in combination with an extended life cycle sustainable cost analysis was used to compare three clinker-to-binder weight ratios (50%, 35% and 25%) and three types of synthetic fiber: polypropylene, polyethylene terephthalate and ultra-high molecular weight polyethylene. This analysis also considers monetized environmental externalities. A mechanical performance indicator (work-to-fracture) was prioritized as the functional unit to meet the needs of engineering practice. This integrated framework revealed trade-offs and opportunities in material selection and optimization.

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with a T4-selective DNA aptamer via a 3-Triethoxysilyl propylsuccinic Anhydride (TESPSA)-mediated silanization approach, enabling a streamlined two-step modification for oriented immobilization. The biosensor achieves robust real-time detection of T4 across the physiological concentration range (5–30 pM), with a limit of detection of ~5 pM and a strong linear correlation between drain current and analyte concentration (R2 = 0.9931). Specificity is confirmed using non-functionalized devices and estradiol as a non-target control. All measurements were performed in undiluted phosphate-buffered saline, representing a physiologically relevant ionic environment and demonstrating stable sensor performance under realistic buffer conditions. The dose–response behavior follows a Hill model, allowing extraction of binding parameters and confirming that the electrical signal originates from specific aptamer–target interactions. These results demonstrate that aptamer-functionalized SiNW-FETs provide a highly sensitive, selective, and miniaturizable platform for quantitative thyroid hormone monitoring, with strong potential for future point-of-care applications.

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.

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 Textile bzw. nichtmetallische Bewehrungen gelten als vielversprechende Alternative zu Stahlbewehrungen. Während gerippte Stahlstäbe ihre Kräfte über mechanischen Formschluss in den Beton einleiten, erfolgt die Kraftübertragung glatter getränkter Carbonfilamentgarne überwiegend über Adhäsion. Dies begrenzt die Verbundfestigkeit und erschwert die effiziente Ausnutzung der mechanischen Leistungsfähigkeit. Oberflächenprofilierte Bewehrungsstrukturen auf Basis der Flechttechnologie besitzen daher ein hohes Potenzial für eine verbesserte und universell einsetzbare Kraftübertragung im Betonbau. In dieser Arbeit wurden neuartige, oberflächenstrukturierte Garnkonstruktionen auf Basis der erweiterten Flechttechnik entwickelt, um Verbundbildung, Zug‐ und Verbundverhalten sowie formschlüssige Strukturstabilität gegenüber glatten Carbonfilamentgarnen zu verbessern. Ziel war zudem, neben multiaxial‐kettengewirkten Gitterstrukturen leistungsfähige Garnstrukturen für robotergestützte Garnablage, endlosbewehrten Beton‐3D‐Druck und die Stabfertigung bereitzustellen. Die Ergebnisse der Zug‐ und Verbunduntersuchungen zeigen ein hohes Potenzial vollständig getränkter oberflächenprofilierter Flechtstrukturen mit durchgehendem Faserverlauf als leistungsfähige Betonbewehrung in Garn‐, Gitter‐ oder Stabform. Gegenüber unprofilierten Strukturen konnte der übertragbare Verbundfluss bei 0,5 mm Auszug bei gleichzeitig hoher Zugfestigkeit um 40–450 % gesteigert werden.