Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Association of plasma vitamin A level with type 2 diabetes mellitus:a community aging population-based cross-sectional study

Recent studies indicated that vitamin A(VA)might be involved in the pathology of type 2 diabetes mellitus(T2DM).This cross-sectional study was conducted to explore the association between circulating VA level and T2DM.A total of 1818 subjects aged 50 years old and above were recruited from the community.Binomial logistic regression and restricted cubic spline(RCS)were applied to analyze the association of plasma VA level with the risk of T2DM.Serum VA and lipid-adjusted VA levels of T2DM patients were significantly higher than that of non-T2DM subjects(P<0.05).The ratios of plasma VA/total cholesterol(TC),VA/high-density lipoprotein cholesterol(HDL-c)and VA/low-density lipoprotein cholesterol(LDL-c)were positively associated with the risk of T2DM in the aging population(P<0.05).Compared with the Q1 level,subjects with Q2 to Q3 levels of plasma VA/triglyceride(TG)have decreased risk of T2DM(odds ratio(OR)Q2=0.68,P_(Q2)=0.021;ORQ3=0.59,P_(Q3)<0.01).Our results indicated that the imbalance of circulating lipids and VA might affect the relationship between VA and T2DM.The middle and aging subjects with higher ratios of plasma VA/TC,VA/HDL-c,and VA/LDL-c displayed increased risk for T2DM,but the moderate ratio of VA/TG might protect against risk of T2DM.

Biofabrication of nanocomposite-based scaffolds containing human bone extracellularmatrix for the differentiation of skeletal stem and progenitor cells

Autograft or metal implants are routinely used in skeletal repair.However,they fail to provide long-term clinical resolution,necessitating a functional biomimetic tissue engineering alternative.The use of native human bone tissue for synthesizing a biomimeticmaterial inkfor three-dimensional(3D)bioprintingof skeletal tissueis anattractivestrategyfor tissueregeneration.Thus,human bone extracellular matrix(bone-ECM)offers an exciting potential for the development of an appropriate microenvironment for human bone marrow stromal cells(HBMSCs)to proliferate and differentiate along the osteogenic lineage.In this study,we engineered a novel material ink(LAB)by blending human bone-ECM(B)with nanoclay(L,Laponite®)and alginate(A)polymers using extrusion-based deposition.The inclusion of the nanofiller and polymeric material increased the rheology,printability,and drug retention properties and,critically,the preservation of HBMSCs viability upon printing.The composite of human bone-ECM-based 3D constructs containing vascular endothelial growth factor(VEGF)enhanced vascularization after implantation in an ex vivo chick chorioallantoic membrane(CAM)model.The inclusion of bone morphogenetic protein-2(BMP-2)with the HBMSCs further enhanced vascularization and mineralization after only seven days.This study demonstrates the synergistic combination of nanoclay with biomimetic materials(alginate and bone-ECM)to support the formation of osteogenic tissue both in vitro and ex vivo and offers a promising novel 3D bioprinting approach to personalized skeletal tissue repair.

Determination of Natural Logarithm of Diffusion Coefficient and Activation Energy of Thin Layer Drying Process of Ginger Rhizome Slices

This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy were determined. For this purpose, the experiments were done at six levels of varied temperatures: 10°C, 20°C, 30°C, 40°C, 50°C and 60°C. The values of effective diffusion coefficients obtained in this work for the variously treated ginger rhizomes closely agreed with the average effective diffusion coefficients of other notable authors who determined the drying kinetics and convective heat transfer coefficients of ginger slices.

Modulatory effects of Lactiplantibacillus plantarum on chronic metabolic diseases

The increased global incidence of chronic metabolic diseases,a vital threat to human health and a burden on our healthcare systems,includes a series of clinical metabolic syndromes such as obesity,diabetes,hypertension,and dyslipidemia.One of the well-known probiotic microorganisms,Lactiplantibacillus plantarum plays an important role in promoting human health,including inhibiting the occurrence and development of a variety of chronic metabolic diseases.The present study provides an overview of the preventive and therapeutic effects of L.plantarum on diabetes,obesity,non-alcoholic fatty liver disease,kidney stone disease,and cardiovascular diseases in animal models and human clinical trials.Ingesting L.plantarum demonstrated its ability to reduce inflammatory and oxidative stress levels by regulating the production of cytokines and short-chain fatty acids(SCFAs),the activity of antioxidant enzymes,and the balance of intestinal microbial communities to alleviate the symptoms of chronic metabolic diseases.Furthermore,updated applications and technologies of L.plantarum in food and biopharmaceutical industries are also discussed.Understanding the characteristics and functions of L.plantarum will guide the development of related probiotic products and explore the modulatory benefit of L.plantarum supplementations on the prevention and treatment of multiple chronic metabolic diseases.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

Data Analysis of Network Parameters for Secure Implementations of SDN-Based Firewall

Software-Defined Networking(SDN)is a new network technology that uses programming to complement the data plane with a control plane.To enable safe connection,however,numerous security challenges must be addressed.Flooding attacks have been one of the most prominent risks on the internet for decades,and they are now becoming challenging difficulties in SDN networks.To solve these challenges,we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system.This study offers a systematic strategy for wrapping up the examination of SDN operations.The Mininet simulator examines the effectiveness of SDN-based firewalls at various network tiers.The fundamental network characteristics that specify how SDN should operate.The three main analytical measures of the network are jitter,response time,and throughput.During regular operations,their behavior evaluates in the standard SDN conditions of Transmission Control Protocol(TCP)flooding and User Datagram Protocol(UDP)flooding with no SDN occurrences.Low Orbit Ion Cannon(LOIC)is applied to launch attacks on the transmission by the allocated server.Wireshark and MATLAB are used for the behavioral study to determine how sensitive the parameters are used in the SDN network and monitor the fluctuations of those parameters for different simulated scenarios.

A practical framework for performance-based reliability analysis of subway stations based on a faultestructure combined system

It is necessary to pay particular attention to the uncertainties that exist in an engineering problem to reduce the risk of seismic damage of infrastructures against natural hazards.Moreover,certain structural performance levels should be satisfied during strong earthquakes.However,these performance levels have been only well described for aboveground structures.This study investigates the main uncertainties involved in the performance-based seismic analysis of a multi-story subway station.More than 100 pulse-like and no pulse-like ground motions have been selected.In this regard,an effective framework is presented,based on a set of nonlinear static and dynamic analyses performed by OpenSees code.The probabilistic seismic demand models for computing the free-field shear strain of soil and racking ratio of structure are proposed.These models result in less variability compared with existing relations,and make it possible to evaluate a wider range of uncertainties through reliability analysis in Rtx software using the Monte Carlo sampling method.This work is performed for three different structural performance levels(denoted as PL1ePL3).It is demonstrated that the error terms related to the magnitude and location of earthquake excitations and also the corresponding attenuation relationships have been the most important parameters.Therefore,using a faultestructure model would be inevitable for the reliability analysis of subway stations.It is found that the higher performance level(i.e.PL3)has more sensitivity to random variables than the others.In this condition,the pulse-like ground motions have a major contribution to the vulnerability of subway stations.

A machine learning approach for accelerated design of magnesium alloys. Part A:Alloy data and property space

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.

Development of Features for Early Detection of Defects and Assessment of Bridge Decks

Damage detection is an important area with growing interest in mechanical and structural engineering.One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations.Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies,mode shapes,and frequency responses.This study aimed at developing a technique based on energy Curvature Difference,power spectrum density,correlation-based index,load distribution factor,and neutral axis shift to assess the bridge deck condition.In addition to tracking energy and frequency over time using wavelet packet transform,in order to further demonstrate the feasibility and validity of the proposed technique for bridge condition assessment,experimental strain data measured from two stages of a bridge in the different intervals were used.The comparative analysis results of the bridge in first and second stage show changes in the proposed feature values.It is concluded,these changes in the values of the proposed features can be used to assess the bridge deck performance.

The Role of Deep Learning in Parking Space Identification and Prediction Systems

In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space.This explains the need to predict the availability of parking spaces.Recently,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction systems.However,no work reviews DL approaches applied to solve parking identification and prediction problems.Inspired by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking spaces.Ataxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are presented.Moreover,several open research challenges are outlined.This work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction problems.Moreover,there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain.This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities.This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

Detailed Quantum Mechanical QSAR Analysis of Certain Aminopyrimidoisoquinolinequinones with Anticancer Activity

A detailed quantum mechanical analysis of electronic disposition of five aminopyrimidoisoquinolinequinones (APIQs) was performed after extraction of this subset of compounds from a larger data set of APIQs via a reported clustering methodology (Elfaki, et al. 2020). Both semi empirical PM3 method and DFT quantum mechanical methods were used to calculate global and local quantum mechanical descriptors (QMDs) to define the electronic environment of these molecules in attempt to rationalize their observed anti-cancer response variability. The biological response is the anticancer activity against human gastric adenocarcenoma (AGS) cell line. The correlation matrix between the calculated global electronic descriptors and biological activity demonstrated that the global dipole moment gives the highest correlation. The local electronic environment was analysed by The Mullikan charges (MC) and Fukui functions for N-5, C-6, C-8 in addition to the N atom of phenylamino side group at C-8. MCs furnished no useful information as each of these atoms had almost identical MC values for all the five compounds with exception of C-6 which gave varied values. Regressing MCs of C-6 against the response traces 60% of the latter variability. As C-6 is an extra annular methyl carbon adjacent to N-5 in isoquinoline residue of APIQ, we reasoned that the chemical reactivities of 4 out of the 5 APIQs might be due to a Chichibabin-type tautomerism implying a possible alkylation aspect in their mechanism of action. The corresponding Fukui functions (f-, f+ and f0) showed a considerable consistency with the patterns of chemical reactivity exhibited by this small set of APIQs.

Decision-Making Information System for Academic Careers in Congolese Universities: From Analysis to Design of a Data Warehouse

Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of several disparate files. This makes it difficult to use this data efficiently and profitably. The aim of this study is to develop this transactional database-based information system into a data warehouse-oriented system. This tool will be able to collect, organize and archive data on the student’s career path, year after year, and transform it for analysis purposes. In the age of Big Data, a number of artificial intelligence techniques have been developed, making it possible to extract useful information from large databases. This extracted information is of paramount importance in decision-making. By way of example, the information extracted by these techniques can be used to predict which stream a student should choose when applying to university. In order to develop our contribution, we analyzed the IT information systems used in the various universities and applied the bottom-up method to design our data warehouse model. We used the relational model to design the data warehouse.

Thermal Characteristics of Structural Lightweight Concrete

A higher cooling load is required with an increas­ing room temperature that resulted from the high thermal conductivity and low time lag of conven­tional construction materials[1].Such a high cooling load increases the carbon footprint from the energy consumption during building performance.The con­dition can be worsened with the urban heat island phenomenon,as the cooling load prolongs to night time for maintaining indoor thermal comfort.

Obtaining 2D Soil Resistance Profiles from the Integration of Electrical Resistivity Data and Standard Penetration Test (SPT) and Light Dynamic Penetrometer (DPL) Resistance Tests—Applications in Mass Movements Studies

In Brazil and various regions globally, the initiation of landslides is frequently associated with rainfall;yet the spatial arrangement of geological structures and stratification considerably influences landslide occurrences. The multifaceted nature of these influences makes the surveillance of mass movements a highly intricate task, requiring an understanding of numerous interdependent variables. Recent years have seen an emergence in scholarly research aimed at integrating geophysical and geotechnical methodologies. The conjoint examination of geophysical and geotechnical data offers an enhanced perspective into subsurface structures. Within this work, a methodology is proposed for the synchronous analysis of electrical resistivity geophysical data and geotechnical data, specifically those extracted from the Light Dynamic Penetrometer (DPL) and Standard Penetration Test (SPT). This study involved a linear fitting process to correlate resistivity with N10/SPT N-values from DPL/SPT soundings, culminating in a 2D profile of N10/SPT N-values predicated on electrical profiles. The findings of this research furnish invaluable insights into slope stability by allowing for a two-dimensional representation of penetration resistance properties. Through the synthesis of geophysical and geotechnical data, this project aims to augment the comprehension of subsurface conditions, with potential implications for refining landslide risk evaluations. This endeavor offers insight into the formulation of more effective and precise slope management protocols and disaster prevention strategies.

Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations.The BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security threats.Meanwhile,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of trust.The decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing mechanisms.In this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their causes.Additionally,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security threats.Finally,we discuss the challenges posed by BGP security problems and outline prospects for future research.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.

Testing and modeling of cyclically loaded rock anchors

The Norwegian Public Roads Administration(NPRA) is planning for an upgrade of the E39 highway route at the westcoast of Norway. Fixed links shall replace ferries at seven fjord crossings. Wide spans and large depths at the crossings combined with challenging subsea topography and environmental loads call for an extension of existing practice. A variety of bridge concepts are evaluated in the feasibility study. The structures will experience significant loads from deadweight, traffic and environment. Anchoring of these forces is thus one of the challenges met in the project. Large-size subsea rock anchors are considered a viable alternative. These can be used for anchoring of floating structures but also with the purpose of increasing capacity of fixed structures. This paper presents first a thorough study of factors affecting rock anchor bond capacity. Laboratory testing of rock anchors subjected to cyclic loading is thereafter presented. Finally, the paper presents a model predicting the capacity of a rock anchor segment, in terms of a ribbed bar, subjected to a cyclic load history. The research assumes a failure mode occurring in the interface between the rock anchor and the surrounding grout. The constitutive behavior of the bonding interface is investigated for anchors subjected to cyclic one-way tensile loads. The model utilizes the static bond capacity curve as a basis, defining the ultimate bond sbuand the slip s1 at τ. A limited number of input parameters are required to apply the model. The model defines the bond-slip behavior with the belonging rock anchor capacity depending on the cyclic load level(τcy/τ), the cyclic load ratio(R= τcy/τcy), and the number of load cycles(N). The constitutive model is intended to model short anchor lengths representing an incremental length of a complete rock anchor.