Hasil untuk "Bridge engineering"

Lei Ran, Zhuwei Li, Bei Ran et al.

Solar carbon dioxide (CO2) conversion is an emerging solution to meet the challenges of sustainable energy systems and environmental/climate concerns. However, the construction of isolated active sites not only influences catalytic activity but also limits the understanding of the structure-catalyst relationship of CO2 reduction. Herein, we develop a universal synthetic protocol to fabricate different single-atom metal sites (e.g., Fe, Co, Ni, Zn, Cu, Mn, and Ru) anchored on the triazine-based covalent organic framework (SAS/Tr-COF) backbone with the bridging structure of metal-nitrogen-chlorine for high-performance catalytic CO2 reduction. Remarkably, the as-synthesized Fe SAS/Tr-COF as a representative catalyst achieved an impressive CO generation rate as high as 980.3 μmol g-1 h-1 and a selectivity of 96.4%, over approximately 26 times higher than that of the pristine Tr-COF under visible light irradiation. From X-ray absorption fine structure analysis and density functional theory calculations, the superior photocatalytic performance is attributed to the synergic effect of atomically dispersed metal sites and Tr-COF host, decreasing the reaction energy barriers for the formation of *COOH intermediates and promoting CO2 adsorption and activation as well as CO desorption. This work not only affords rational design of state-of-the-art catalysts at the molecular level but also provides in-depth insights for efficient CO2 conversion.

Mattia Francesco Bado, J. Casas

The present work is a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS). The latter are cutting-edge strain, temperature and vibration monitoring tools with a large potential pool, namely their minimal intrusiveness, accuracy, ease of deployment and more. Its most state-of-the-art feature, though, is the ability to perform measurements with very small spatial resolutions (as small as 0.63 mm). This review article intends to introduce, inform and advise the readers on various DOFS deployment methodologies for the assessment of the residual ability of a structure to continue serving its intended purpose. By collecting in a single place these recent efforts, advancements and findings, the authors intend to contribute to the goal of collective growth towards an efficient SHM. The current work is structured in a manner that allows for the single consultation of any specific DOFS application field, i.e., laboratory experimentation, the built environment (bridges, buildings, roads, etc.), geotechnical constructions, tunnels, pipelines and wind turbines. Beforehand, a brief section was constructed around the recent progress on the study of the strain transfer mechanisms occurring in the multi-layered sensing system inherent to any DOFS deployment (different kinds of fiber claddings, coatings and bonding adhesives). Finally, a section is also dedicated to ideas and concepts for those novel DOFS applications which may very well represent the future of SHM.

W. Lai, Xavier Dérobert, P. Annan

Abstract The GPR (Ground Penetrating Radar) conference in Hong Kong year 2016 marked the 30th anniversary of the initial meeting in Tifton, Georgia, USA on 1986. The conference has been being a bi-annual event and has been hosted by sixteen cities from four continents. Throughout these 30 years, researchers and practitioners witnessed the analog paper printout to digital era that enables very efficient collection, processing and 3D imaging of large amount of data required in GPR imaging in infrastructure. GPR has systematically progressed forward from “Locating and Testing” to “Imaging and Diagnosis” with the Holy Grail of ’Seeing the unseen’ becoming a reality. This paper reviews the latest development of the GPR’s primary infrastructure applications, namely buildings, pavements, bridges, tunnel liners, geotechnical and buried utilities. We review both the ability to assess structure as built character and the ability to indicate the state of deterioration. Finally, we outline the path to a more rigorous development in terms of standardization, accreditation, and procurement policy.

Y. Bao, Zhicheng Chen, Shiyin Wei et al.

Abstract Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.

Silverio Mart'inez-Fern'andez, J. Bogner, Xavier Franch et al.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

Na Xin, Jianxin Guan, Chenguang Zhou et al.

Over the past two decades, various techniques for fabricating nano-gapped electrodes have emerged, promoting rapid development in the field of single-molecule electronics, on both the experimental and theoretical sides. To investigate intrinsic quantum phenomena and achieve desired functionalities, it is important to fully understand the charge transport characteristics of single-molecule devices. In this Review, we present the principles that have been developed for fabricating reliable molecular junctions and tuning their intrinsic properties from an engineering perspective. Through holistic consideration of the device structure, we divide single-molecule junctions into three intercorrelated components: the electrode, the contact (spacer–linker) interface and the molecular backbone or functional centre. We systematically discuss the selection of the electrode material and the design of the molecular components from the point of view of the materials, the interface and molecular engineering. The influence of the properties of these elements on the molecule–electrode interface coupling and on the relative energy gap between the Fermi level of the electrode and the orbital energy levels of the molecule, which directly influence the charge transport behaviour of single-molecule devices, is also a focus of our analysis. On the basis of these considerations, we examine various functionalities demonstrated in molecular junctions through molecular design and engineering.In this Review, the principles developed for fabricating reliable molecular junctions and tuning their intrinsic properties are examined from the point of view of the electrode, the interface and molecular engineering. The various functionalities demonstrated in molecular junctions through molecular design are discussed, along with the open challenges in the field.Key pointsSingle-molecule electronics has become a burgeoning subfield of nanoscience and has begun to develop beyond the basic description of carrier transport, expanding in different research directions.A single-molecule junction can be divided into three intercorrelated components: the electrode, the contact interface and the molecular backbone or functional centre.Both the mechanical stability and electronic coupling of the molecule–electrode interface increase with the binding energy of the electrode–anchoring moiety interaction. A compromise between these factors can be achieved by inserting suitable spacers between the molecular kernel and anchoring groups.To select suitable electrode materials, chemical inertness to air, good processability, suitable work function and good compatibility with molecules should be taken into consideration.The structures of molecular bridges can be tuned by the molecular length, the geometry of the main chains, the responsivity of the functional centres and the types of side groups, offering opportunities to probe intrinsic physical properties and realize various functionalities.Challenges in the field of single-molecule electronics include improving device-to-device uniformity, stability, integration capability and accuracy of theoretical models.

Chen Xu, B. Cao, Yong Yuan et al.

Recently, a class of machine learning methods called physics-informed neural networks (PINNs) has been proposed and gained prevalence in solving various scientific computing problems. This approach enables the solution of partial differential equations (PDEs) via embedding physical laws into the loss function. Many inverse problems can be tackled by simply combining the data from real life scenarios with existing PINN algorithms. In this paper, we present a multi-task learning method using uncertainty weighting to improve the training efficiency and accuracy of PINNs for inverse problems in linear elasticity and hyperelasticity. Furthermore, we demonstrate an application of PINNs to a practical inverse problem in structural analysis: prediction of external loads of diverse engineering structures based on limited displacement monitoring points. To this end, we first determine a simplified loading scenario at the offline stage. By setting unknown boundary conditions as learnable parameters, PINNs can predict the external loads with the support of measured data. When it comes to the online stage in real engineering projects, transfer learning is employed to fine-tune the pre-trained model from offline stage. Our results show that, even with noisy gappy data, satisfactory results can still be obtained from the PINN model due to the dual regularization of physics laws and prior knowledge, which exhibits better robustness compared to traditional analysis methods. Our approach is capable of bridging the gap between various structures with geometric scaling and under different loading scenarios, and the convergence of training is also greatly accelerated through not only the layer freezing but also the multi-task weight inheritance from pre-trained models, thus making it possible to be applied as surrogate models in actual engineering projects.

Yannan Liu, Cheng‐Hao Liu, T. Debnath et al.

Integrating plasmonic nanoparticles into the photoactive metal-organic matrix is highly desirable due to the plasmonic near field enhancement, complementary light absorption, and accelerated separation of photogenerated charge carriers at the junction interface. The construction of a well-defined, intimate interface is vital for efficient charge carrier separation, however, it remains a challenge in synthesis. Here we synthesize a junction bearing intimate interface, composed of plasmonic Ag nanoparticles and matrix with silver node via a facile one-step approach. The plasmonic effect of Ag nanoparticles on the matrix is visualized through electron energy loss mapping. Moreover, charge carrier transfer from the plasmonic nanoparticles to the matrix is verified through ultrafast transient absorption spectroscopy and in-situ photoelectron spectroscopy. The system delivers highly efficient visible-light photocatalytic H_2 generation, surpassing most reported metal-organic framework-based photocatalytic systems. This work sheds light on effective electronic and energy bridging between plasmonic nanoparticles and organic semiconductors. The integration of plasmonic structures with photoactive matrices offers a promising means to enhance solar-to-fuel conversion. Here, the authors bridge plasmonic nanoparticles and metal-organic matrix through interface-engineering to boost photocatalytic hydrogen evolution.

Matteo Torzoni, M. Tezzele, S. Mariani et al.

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

Sunday Ochella, M. Shafiee, F. Dinmohammadi

: Prognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). Methodologies for PHM are either model-driven, data-driven or a fusion of both approaches. Data-driven approaches make extensive use of large-scale datasets collected from physical assets to identify underlying failure mechanisms and root causes. In recent years, many data-driven PHM models have been developed to evaluate system’s health conditions using artificial intelligence (AI) and machine learning (ML) algorithms applied to condition monitoring data. The field of AI is fast gaining acceptance in various areas of applications such as robotics, autonomous vehicles and smart devices. With advancements in the use of AI technologies in Industry 4.0, where systems consist of multiple interconnected components in a cyber-physical space, there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. In this paper, a thorough state-of-the-art review of the AI techniques adopted for PHM of engineering systems is conducted. Furthermore, given that the future of inspection and maintenance will be predominantly AI-driven, the paper discusses the soft issues relating to manpower, cyber-security, standards and regulations under such a regime. The review concludes that the current systems and methodologies for maintenance will inevitably become incompatible with future designs and systems; as such, continued research into AI-driven prognostics systems is expedient as it offers the best promise of bridging the potential gap.

Pengzuo Chen, Nan Zhang, Sibo Wang et al.

Significance Ammonia is one of the most important chemical raw materials with an annual production exceeding 200 million tons. The Haber–Bosch process is still the dominant route for industrial ammonia synthesis, which consumes 1∼3% of global annual energy production and represents a significant contributor to climate change. Electrocatalytic N2 reduction reaction is an attractive alternative candidate for carbon-free and sustainable NH3 production, but often suffers from low efficiency. Here, we developed an interfacial engineering strategy for preparing a class of strongly coupled hybrid electrocatalysts for N2 fixation. The hybrids exhibit superior N2 reduction reaction activity with a high NH3 Faradaic efficiency of 25.9% under ambient conditions. This strategy provides an approach to design advanced materials for ammonia production. Electrocatalytic N2 reduction reaction (NRR) into ammonia (NH3), especially if driven by renewable energy, represents a potentially clean and sustainable strategy for replacing traditional Haber–Bosch process and dealing with climate change effect. However, electrocatalytic NRR process under ambient conditions often suffers from low Faradaic efficiency and high overpotential. Developing newly regulative methods for highly efficient NRR electrocatalysts is of great significance for NH3 synthesis. Here, we propose an interfacial engineering strategy for designing a class of strongly coupled hybrid materials as highly active electrocatalysts for catalytic N2 fixation. X-ray absorption near-edge spectroscopy (XANES) spectra confirm the successful construction of strong bridging bonds (Co–N/S–C) at the interface between CoSx nanoparticles and NS-G (nitrogen- and sulfur-doped reduced graphene). These bridging bonds can accelerate the reaction kinetics by acting as an electron transport channel, enabling electrocatalytic NRR at a low overpotential. As expected, CoS2/NS-G hybrids show superior NRR activity with a high NH3 Faradaic efficiency of 25.9% at −0.05 V versus reversible hydrogen electrode (RHE). Moreover, this strategy is general and can be extended to a series of other strongly coupled metal sulfide hybrids. This work provides an approach to design advanced materials for ammonia production.

C. Fan, Yongjun Sun, Yang Zhao et al.

Abstract The enrichment in building operation data has enabled the development of advanced data-driven methods for building energy predictions. Existing studies mainly focused on the utilization of supervised learning techniques for model development, while overlooking the significance of feature engineering. Feature engineering are helpful for reducing data dimensionality, decreasing prediction model complexity, and tackling the problem of corrupted and noisy information. Considering that each building has unique operating characteristics, it is neither practical nor efficient to manually identify features for model developments. Data-driven feature engineering methods are thus needed to ensure the flexibility and generalization of building energy prediction models. Using operation data of real buildings, this paper investigates the performance of different deep learning techniques in automatically deriving high-quality features for building energy predictions. Three types of deep learning-based features are developed using fully-connected autoencoders, convolutional autoencoders and generative adversarial networks respectively. Their potentials in building energy predictions have been exploited and compared with conventional feature engineering methods. The study validates the usefulness of deep learning in enhancing building energy prediction performance. The research results help to automate and improve the predictive modeling process while bridging the knowledge gaps between deep learning and building professionals.

Kun Wu, Xin-Yi Liu, Pei-Wen Cheng et al.

Interfacial charge transfer on the surface of heterogeneous photocatalysts dictates the efficiency of reactive oxygen species (ROS) generation and therefore the efficiency of aerobic oxidation reactions. Reticular chemistry in metal-organic frameworks (MOFs) allows for the rational design of donor-acceptor pairs to optimize interfacial charge-transfer kinetics. Herein, we report a series of isostructural fcu-topology Ni8-MOFs (termed JNU-212, JNU-213, JNU-214, and JNU-215) with linearly bridged bipyrazoles as organic linkers. These crystalline Ni8-MOFs can maintain their structural integrity in 7 M NaOH at 100 °C for 24 h. Experimental studies reveal that linker engineering by tuning the electron-accepting capacity of the pyrazole-bridging units renders these Ni8-MOFs with significantly improved charge separation and transfer efficiency under visible-light irradiation. Among them, the one containing a benzoselenadiazole unit (JNU-214) exhibits the best photocatalytic performance in the aerobic oxidation of benzylamines with a conversion rate of 99% in 24 h. Recycling experiments were carried out to confirm the stability and reusability of JNU-214 as a robust heterogeneous catalyst. Significantly, the systematic modulation of the electron-accepting capacity of the bridging units in donor-acceptor-donor MOFs provides a new pathway to develop viable noble-metal-free heterogeneous photocatalysts for aerobic oxidation reactions.

Junke Lin, Genhao Shi, Xiangyao Fu et al.

The aim of this study is to investigate the influence of Mo on the microstructure and mechanical properties of railway frog steel. To address the challenges of a coarse microstructure and alloy element segregation caused by the current casting method of railway frog steel, the application of thermal mechanical control process (TMCP) technology can achieve a uniform and refined microstructure and stable mechanical properties, which is progress for the development of high-manganese railway frog steel. The TMCP of four experimental steels with varying Mo contents of 0.02~1.01 wt.% was simulated using a Gleeble 3500. The mechanical properties were tested, and the microstructure of each sample was characterized. A single austenite formed in each Mo-containing steel. With the increased Mo content, the grain boundary carbides decreased due to the formation of carbides within the grains, and the austenite and twin sizes were refined. Moreover, grain boundary strengthening and dislocation strengthening increased, while solid solution strengthening and precipitation strengthening had little effect, leading to an increase in the final yield strength. The contribution of dislocation strengthening to the yield strength was 51~56%, indicating that dislocation strengthening was the most significant strengthening method in the high-manganese railway frog steel produced using the TMCP. The impact energy showed a trend of first increasing and then decreasing, and the impact energy reached the highest point when the Mo content was 0.30 wt.%. In addition, the mechanisms governing the effect of increased Si in controlling the final microstructure and mechanical properties are discussed.

Miguel A. Oltra-Rodríguez, Paul Stonehouse, Nicolas Afonso-Alonso et al.

The increasing digitalization of the world underscores the critical importance of both social and technical aspects in software engineering practice. While prior research links socio-technical congruence (STC) to positive workstream outcomes, the current convergence of digital products, technologies, and social systems introduces novel and often unpredictable results, driven by the complex interplay of leadership, organizational culture, and software engineering practices operating as a complex adaptive system (CAS). This paper proposes a novel model for adopting socio-cultural practices to bridge the social and technical divide through the lens of STC. The innovation of the model lies in its socio-technical evolutionary journey, built upon dual systems: (1) an analytical System-I focused on enhancing robustness via compliance with Lean and Agile socio-cultural practices, and (2) a holistic System-II emphasizing resilience through an acceptance of interdependence of system actors that requires sense-making techniques. A methodology based on this model was piloted across six case studies: three in an Enterprise IT organization and three in two business units undergoing transformations on Lean and Agile plus DevOps adoption. System-I’s robustness was evaluated through surveys and structured STC maturity assessments (self and guided ones). System-II employed sense-making techniques to foster resilience within the system of work (SoW), laying the groundwork for their evolutionary journeys. The findings reveal a significant need for greater alignment between management (as transformation agents) and software engineering practices. However, the study suggests actionable guidelines, grounded in new principles and mental models for operating within a CAS, to cultivate enhanced resilience and robustness in a VUCA world.

Ming Wai Yeung, Rutger R. van de Leur, Jan Walter Benjamins et al.

Summary: Conventional approaches to analyzing electrocardiograms (ECG) in discrete parameters (such as the PR interval) ignored the high dimensionality of data omitted subtle but relevant information. We applied a variational auto-encoder to learn the underlying distributions of the ECG of 41,927 UK Biobank participants, generating 32-dimensional representation (latent factors). The latent factors showed correlations to conventional ECG parameters and strong associations to cardiac phenotypes estimated from magnetic resonance imaging. We found definitive associations of the latent factors to conduction, rhythm, and structural disorders (all p < 4.51 × 10−308) and additionally value in mortality prediction. Genome wide association study (GWAS) of the latent factors, revealed 170 genetic loci with 29 not previously associated with electrocardiographic phenotypes. Further characterization of the genetic signals suggested involvement in cardiac development, contractility, and electrophysiology. Our results supported that the deep representation learning of 12-lead ECG could provide clinically meaningful and interpretable insights into cardiovascular biology and health.

Piotr Smolira, Jan Karlshøj

Operation and maintenance of civil infrastructure facilities such as bridges is the most extended period of the entire lifetime of the structures. This phase provides many opportunities that benefit society. However, such a wide span of operation also exposes bridges to various threats and risks. Therefore, knowledge domains such as Bridge Management System and life-cycle management are crucial ingredients for maintaining the level of performance of bridges and their components. Bridge Management System (BMS), since its emergence in 1975, has been constantly evolving to meet the needs of the industry with advancements in technology through new paradigms. To accelerate the process of creating and managing the data and information about bridge structures, the terms Bridge Information Modeling (BRiM) and Civil Information Modeling have appeared more frequently. Inspired by Building Information Modeling, the incentive is to manage the information better, from the concept until the end-of-life. The amount of created data is extensive and versatile. To address the issue of potential unstructured and heterogeneous information, academic and industrial researchers have been developing classifications, categories, and taxonomies. Given the advancements and growth of Semantic Web technologies, and qualities such as interoperability, machine-readable format, and extensibility, ontology development has become prominent. Current experience and success in creating and adapting ontologies into BIM workflow set examples for other branches in the built environment like civil engineering. Ontologies describing various areas of the bridge domain have been developed. However, proposals of how such information models could be aligned and integrated are seldom seen. This paper presents scoped evaluation of ontologies from bridge operation and maintenance domain. It gives an overview of how well different subjects are compliment entire topic, and it provides recommendations on modeling and evaluating ontologies related to a particular use case. It proposes a methodology that can be used for further development, alignment, and finding ontology gaps in the bridge domain.

Pei Duan, Liu Yang, Xin Luo et al.

Converter-dominated power systems play a key role in renewable energy integration, yet selecting suitable converter topologies under complex operating and fault conditions remains challenging. This brief study introduces a multi-criteria response modeling framework for evaluating modular multilevel converter (MMC) topologies. The method models nonlinear relationships between engineering indicators—voltage difference, reliability, compactness, cost, and control complexity—and topology adaptability using linear, saturating, and bell-shaped response functions. A hybrid weighting mechanism combining analytic hierarchy process and entropy theory balances expert judgment with data variability. Case studies across distributed renewable access, rail traction, and inter-city HVDC systems verify the framework’s capability to capture topology–scenario matching. Results indicate that half-bridge MMCs excel in compact low-cost applications, symmetric hybrids perform best in high-reliability scenarios, and asymmetric hybrids show advantages under high-voltage interconnection requiring fault ride-through. The proposed method provides a concise, data-driven decision tool for renewable-dominated converter selection, supporting future HVDC planning and flexible grid development.

Huawei Qu

Abstract Large bone defects, which occur due to various causes, have substantially affected people's health and quality of life. Bone tissue engineering (BTE) is a promising approach for repairing or replacing bone injuries. The aim of BTE scaffolds is to mimic the biological function and structure of the natural bone extracellular matrix (ECM), which provides a three-dimensional (3D) environment for cell adsorption, proliferation and differentiation. Significant advances in materials science, computer-aided design (CAD) and biomedical engineering have facilitated BTE scaffolds. This paper describes the requirements of BTE scaffolds and highlights the important role of additive manufacturing (AM) technologies in building bridges between biomaterials, CAD models and additives, and BTE scaffolds. It reviews various AM technologies that are used to fabricate BTE scaffolds. These technologies are divided into seven categories: (1) stereolithography (SLA), (2) powder bed fusion (PBF), (3) binder jetting (BJ), (4) material extrusion (ME), (5) material jetting (MJ), (6) volumetric printing (VP) and (7) 4D printing (4DP). The characteristics, raw materials, accuracy, cost, advantages and disadvantages of the AM technologies are discussed. Several recommendations for future research are presented.

Philip G. Jordan, P. Fröhle

Under the umbrella term of Nature-based Solutions (NbS) fall measures from a wide range of disciplines. With regard to coastal protection, coastal ecosystems represent possible and promising NbS to coastal threats such as storm surges or erosion. Around the globe, the looming climate change and related developments in the coastal landscapes as well as a paradigm shift in societal views shifted the focus of decision-makers and researchers onto NbS for coastal protection, driving the need for a comprehensive up-to-date review of coastal ecosystems like salt marshes, mangroves, seagrass meadows, beaches, dunes, coral, and shellfish/oyster reefs and their benefits for Water, Nature and People alike. While existing reviews of NbS have mainly focused on the idea of softer coastal protection in general and constraints regarding management and regulations, this study reviews not only the characteristics, features and needs of the coastal ecosystems under consideration but also examines the ecosystems’ potential and related processes for coastal protection, their ecological as well as their societal benefits. This review paper is based on an extensive literature review and analysis of scientific publications, books and book sections, guidelines, reports, policy recommendations and strategies. In order to create a basis for the selection of site-suitable adaptation measures for local coastal challenges and questions, this study compiles the coastal ecosystems’ key features and elaborates the provided ecosystem services for protective, ecological and societal needs. The highlighted diversity of processes within ecosystems that directly cause or support coastal protection, in combination with the multiple ecological services and societal benefits, underlines the great potential of coastal ecosystems to bridge the gap between coastal engineering and nature conservation. In combination with existing coastal protection, coastal ecosystems as NbS can serve both disciplines equally and provide an integral, sustainable element in the adaptation of coastal protection to climate change.