Accurate Structural Analysis Research Articles

Complexation of β-conglycinin or glycinin with sodium alginate blocks: Complexation mechanism and structural and functional properties

Sodium alginate (SA), α1 → 4 linked copolymer of β-d-mannuronic acid (M) and α-L guluronic acid (G) forms two homopolymeric fractions (MM and GG) and a heteropolymeric fraction (MG). The main components of soybean protein isolate are β-conglycinin (7S) and glycinin (11S). However, accurate structural analyses of the 7S/11S and MM/MG/GG complexes are lacking. The complexation mechanism, structure, and functional properties of the complexes of 7S/11S with SA blocks was investigated at pH 4. The number of intermolecular hydrogen bonds exceeded that of the intramolecular hydrogen bonds. Secondary and tertiary structures and molecular weights of the complexes were significantly different from those of 7S/11S. The crystalline structure transformed to an amorphous structure, and the complexes underwent fluorescence quenching. Complexes 11S-MM and 11S-MG exhibited good emulsifying properties of 37.88 % and 38.13 %, respectively; 7S-GG and 7S-MM exhibited excellent surface hydrophobicity and emulsifying properties; and 11S-MM, 11S-GG, and 11S-MG exhibited excellent thermal stability.

An atomistic entropy based finite element multiscale method for modeling amorphous materials

A new concurrent multiscale method based on the maximum entropy statistical method is proposed for the analysis of amorphous materials. In addition to reducing the number of degrees of freedom, any irregular structure of amorphous materials can be accurately analyzed. The amorphous structure is generated from a solid crystalline structure by a heating/cooling process without the need for any specific independent technique to create such a random structure. The method is expected to perform efficiently because of its entropic and irregular intrinsic. Regions with moderate conditions are discretized by the entropy-based finite element method while the severe parts are simulated by the present atomistic-based multiscale technique. The new proposed approach allows for accurate analysis of amorphous structures across multiple scales and does not suffer from conventional complications such as the standard Cauchy Born rule and consistency of the molecular structure with the standard finite element geometries.The conventional Cauchy-Born rule cannot be directly used due to the non–crystalline microstructure of the material. A remedy is proposed based on the meshfree techniques by constructing a continuous atomic deformation field from the imposed macro deformation gradient. The resultant deformation gradient and the stress field remain consistent in micro scales. In addition, a genetic algorithm-based method, which has less sensitivity to the choice of initial point and number of parameters, is adopted for the maximization of the entropy function.The silicon amorphous structure is considered for MD simulations. It is obtained by quenching from a melted sample. The MD-obtained structure is further analyzed and the predicted displacements and stress contours, as well as the density and radial distribution functions are examined to assess the state of the material. Then, the proposed meshfree technique is applied to construct the continuous form of the deformation gradient on the MD model to improve the accuracy of the solution. The proposed concurrent multiscale method is verified and then employed to simulate an amorphous silicon specimen. Finally, the effects of sample size, strain rate and quenching speed on rupture stress and strain in different 3D tensile simulations are investigated by the proposed multiscale method.

Probabilistic approach to assess URM walls with openings using discrete rigid block analysis (D-RBA)

This study aims to improve our current understanding of the seismic assessment of load-bearing unreinforced masonry (URM) systems by proposing a probabilistic computational modeling framework using the discrete element method (DEM). The main objective is to predict the structural behavior and capacity of URM walls with openings subjected to lateral loading, considering uncertainties in material properties. The proposed modeling strategy represents masonry as an assembly of rigid blocks interacting along their boundaries by adopting the point-contact hypothesis. Fracture energy-based softening contact models are implemented into a commercial discrete element code (3DEC) to better simulate both the pre- and post-peak behavior of masonry. The results highlight the influence of material properties on the force capacity, displacement capacity (drift limits), and collapse mechanisms of walls with openings. Based on the applied non-spatial probabilistic analyses, the most commonly observed failure mechanisms are further assessed using a simplified macro-block formulation. As a result, practical, yet necessary, inferences are made, providing valuable contributions. Furthermore, the validated discontinuum analysis framework is demonstrated as an accurate structural analysis strategy and a useful approach to simulating the potential collapse mechanism of load-bearing URM structures.

Revisiting alkaline cupric oxide oxidation method for lignin structural analysis.

Lignin structural analysis is important for the comprehensive utilization of lignin as well as delignification and bleaching during pulping while it is difficult to completely elucidate lignin structure due to its structural complexity and heterogeneity. Depolymerization of lignin into simple monomers via alkaline cupric oxide oxidation (OxCuO) followed by chromatographic analysis of the monomers is an effective method for lignin structural analysis. Here we revisited the OxCuO of lignin model compounds (monomers and dimers) and three representative lignocelluloses (i.e., Eucalyptus, Masson pine, and corn stover) to understand the effects of reaction conditions and lignin sub-structures on oxidation product yields and distributions. The improved OxCuO was found to be effective in oxidatively breaking the robust interunit C-C bonds in the β-β′ and β-5′ moieties of lignin other than β-O-4′ linkages at an elevated temperature (210°C). Further degradation of the monomeric oxidation products could also occur to reduce the monomer yields under a severe condition (i.e., high temperature and long reaction time). In addition, O2 inputs could reduce the monomer yields via nonselective overoxidation, thus having negative effects on accurate structural analysis of lignin. The O2 removal via ultrasonication combined with N2 flushing prior to the oxidation reaction could improve the monomer yield about 1.2 times (compared to that without O2 removal) at a low biomass loading of 5 wt%. By using the improved method of OxCuO, a monomer yield of 71.9% could be achieved from Eucalyptus (hardwood) lignin, which was much higher than conventional nitrobenzene oxidation (59.8%) and reductive depolymerization (51.9%). Considering the low cost, high availability, and low toxicity of CuO, the improved OxCuO could be a convenient and economic method for more accurate lignin structural analysis.

The effects of non-structural components on the dynamic characteristics and vulnerability of concrete structures using ambient vibration tests and Nakamura's criterion

From the past up to now, selecting a reliable structural system as well as analyzing and designing it accurately have considerably attracted the attention of researchers and engineers. An appropriate evaluation of the dynamic characteristics of the structures such as natural frequency and damping ratio has a considerable effect on the accurate analysis and design of structures. In addition, the vulnerability assessment of structures plays a significant role in selecting a resilient structural system against the seismicity of the area. In the light of these facts, one of the most important challenges is that the effects of non-structural components (NSCs) are commonly considered as a concentrated/distributed mass in most FE models and their stiffness and damping ratio are ignored. Therefore, this research aims to figure out the effects of NSCs on the dynamic characteristics and the vulnerability indices of concrete structures using microtremor measurements. For this purpose, the ambient vibrations of four concrete buildings (with two bending frame and two shear wall structural systems) during the construction process are measured in three independent stages: (1) after the completion of the structural system; (2) after the completion of the interior and exterior partition walls; (3) after the completion of the flooring, facade, and parapet elements (when the building is completely constructed). Afterward, to extract the dynamic characteristics and to compute the vulnerability indices of the structures, the measured microtremors are subjected to two signal processing techniques, i.e., floor spectral ratio (FSR) and random decrement method (RDM). It is observed that, by taking the effects of NSCs into account, the values of both dynamic characteristics of the concrete structures (i.e., natural frequency and damping ratio) are increased when the buildings are under the erection process. Furthermore, the obtained results for the concrete structures with the bending frame structural system demonstrate that the vulnerability index increases in the second stage compared with the first one, whereas it remarkably decreases in the third stage compared with the second one. Reciprocally, the obtained vulnerability indices for the concrete structures with the shear wall structural system show a similar behavior.

A robust method for load-carrying capacity assessment of semirigid steel frames considering fuzzy parameters

The non-probabilistic approach based on fuzzy logic has been applied in analyzing and assessing steel structures. However, there is still a dearth of studies on the behavior of nonlinear inelastic semirigid frame structures in the presence of fuzzy uncertainty. Moreover, accurate fuzzy analysis of structures often involves high computation cost. In this paper, a robust method for the prediction of the load-carrying capacity of semirigid steel frame structures defined with fuzzy parameters is proposed. For the analysis purpose, the nonlinear inelastic analysis taking account of both material and geometrical nonlinearities is adopted to estimate the ultimate load factor (ULF11ULF is the ultimate load factor of the semi-rigid steel frame structure.) of the structure. Columns and beams are modeled by the beam–column element with a refined plastic hinge at two ends, and the beam-to-column connections are defined by zero-length semirigid joints. Innovatively, a fuzzy estimation procedure named the Moving Extrema Method (MEM) is developed to efficiently capture the variation of ULF due to fuzziness in the structural properties and applied loads. Applications to three semirigid steel frames having a relatively large number of fuzzy parameters are conducted. Numerical results show that the proposed MEM can predict the fuzzy ULF with relatively high accuracy and reasonable computation volume. More insights on the effect of the fuzzy structural parameters on the ULF are also presented.

Reliability-based assessment and optimization for the two-way shear design of lightweight reinforced concrete slabs using the ACI and EC2

Recent brittle two-way shear failures have made assessing strength models a topic of interest. In addition, the economic alternatives have made lightweight reinforced concrete slabs (LWRCS) a popular choice of construction in many areas around the world. However, researchers have rarely tackled the two-way shear strength models of LWRCS using accurate structural reliability analysis methods. Most of the research investigations evaluated the performance of the two-way shear strength models for the LWRCS with respect to an experimental database using statistical measures. While it did not implement structural reliability methods, which could include the variability due to the following: (1) the governing variable’s random distribution; (2) the size of the experimental database; and (3) errors in measurements, construction, and design. Thus, this current study aims to apply structural reliability methods for the two-way shear of LWRCS. Two internationally renowned design codes were selected using an extensive experimental database. A brief outline of existing structural reliability methods is presented and compared. A selected reliability method was used to assess the reliability of the design-chosen codes. The reliability and sensitivity indices were calculated for the American and European design code provisions of two-way shear of lightweight concrete and compared with each other as well as international standard reliability. The American design code were found to be less reliable compared with the European, which might be due to not considering the flexure reinforcement ratio in the model. In addition, model accuracy is the main factor affecting reliability, however, the variability of basic parameters including concrete density, concrete strength and cross section depth have larger effect compared to flexure reinforcement ratio and loading area dimensions. Moreover, in order to achieve target reliability indices value of 3.8, 3.7, or 3.6 a reduction factor value of 0.8, 0.75, or 0.70 could be implemented. These findings could be helpful in design code developments. However, the results are limited to the selected variability of the random variables.

Weight distribution algorithm for global ship analysis in preliminary design stage

Abstract Structural analysis, based on a global ship model, is vital for examining the structural strength and safety of a hull. In the “NASTRAN” global ship model, only the structural members and cargo tank of a ship are modeled and the outfit, electric system, and other tanks and liquid are omitted from the model. The additional weight of these omitted elements, however, constitutes part of the light weight of the ship and must be considered in conducting an accurate structural analysis of the ship. Generally, the weight of these omitted elements is calculated using the trim and stability information of the ship and is then added to the model. In such cases, the center of gravity of the global ship model, which now includes the weight of these omitted elements, must be adjusted to the target center of gravity required for the analysis. In this study, an algorithm was developed to satisfy the target center of gravity while distributing the weight of these omitted elements to the global ship model using Gaussian elimination. The algorithm was developed using C#, and its accuracy was verified by applying it to the global ship models.

Parameter optimization for Pivot hysteresis model for reinforced concrete columns with different failure modes

AbstractReliable hysteretic modeling is essential for the evaluation of existing reinforced concrete (RC) buildings. Also, tall buildings and other structures of high importance require reasonably accurate seismic nonlinear time‐domain analysis for design, which includes load–displacement behavior and hysteretic modeling. However, the hysteretic behavior of RC members is very complex and challenging to predict. Currently, the Pivot hysteresis model is used to predict the hysteretic response of members with parameters α and β representing the unloading stiffness and pinching, respectively, determined using the geometric and reinforcement details in column members. However, the hysteretic response is fairly different for flexure and shear failure modes and must be accounted for to derive accurate hysteretic predictions. In this paper, an effort has been made to improve the existing approach for columns by deriving α and β separately for flexure and shear modes of failure. The chosen variables to control α and β are axial load ratio (ALR), longitudinal and transverse reinforcement indices. Equations for α and β were calibrated through optimization of energy dissipation from 113 column specimens subjected to cyclic loading, using an advanced optimization algorithm known as Simulated Annealing. The proposed formulations can improve the accuracy of the Pivot hysteresis model for column members by capturing pinching and stiffness degradation more reasonably under different failure modes. The proposed approach can aid design engineers in performing a more reliable and accurate analysis of complex structures.

Accurate thermal-induced structural failure analysis under incompressible conditions

In this work, the performance of the mixed 3-field displacement/deviatoric-strain/pressure (u/e/p) finite element is examined for nonlinear thermo-mechanical structural applications under incompressible behavior. The proposed FE model increases the solution accuracy in terms of strains and stresses, guaranteeing mesh-objective results in nonlinear analyses. Structural failure is modelled with J2-plasticity and J2-damage constitutive laws, introducing the isochoric behavior, typical of metals, in the material response. The solution of the coupled thermal and mechanical problems follows a staggered scheme and temperature dependent material properties are introduced to study the effect of the thermal coupling in the mechanical problem. This FE approach is applicable with any interpolation basis: triangles, quadrilaterals, tetrahedras, hexahedras and prisms.A set of numerical benchmark problems is proposed to examine the influence of the enhanced accuracy of the proposed model in thermally-induced structural failure analyses in incompressible conditions. The study includes the comparison of the u/e/p and u/p FE formulations, where the effect of the thermal coupling in the problem is investigated. The superior performance of the 3-field formulation with regard to the evaluation of collapse mechanisms, failure loads, mechanical dissipation and numerical stability in incompressible situations is shown.

Use of digital image correlation to connect fracture curves and sectional analysis for structural design of polyolefin fibre reinforced concrete elements

The boosting of the polyolefin fibre reinforced concrete (PFRC) usage for structural design requires that a connection between the laboratory standards and the structural codes be made. This study aims to fill this gap by combining laboratory mechanical test results, digital image correlation and numerical simulations. The σ-ε and σ-w relations in PFRC elements have been connected by means of a parameter that affects the strain of concrete (lref). lref depends linearly on the crack width and exponentially on the fibre dosage. A procedure that enables an accurate multilayer sectional structural analysis for PFRC structural elements has been obtained.

Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins.

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches, such as AlphaFold2, opens new opportunities to exploit the complexity of these macro-biomolecules for highly specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of anti-CRISPR proteins which are structurally similar to the recently discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

Deep learning approach for classification of chondrocytes in rats

The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, thereby relieving of some burden from pathologists in terms of performing tedious microscopic examination for analysis of a large number of slides for pathological lesions. Recently, the use of these digital methods has expanded into the field of medical image analysis. In this study, the Inception-v3 deep learning model was used for classification of chondrocytes from knee joints of rats. Knee joints were extracted, fixed in neutral buffered formalin, decalcified, processed and embedded in paraffin, and hematoxylin and eosin (H&E) stained. The H&E stained slides were converted into whole slide imaging (WSI), and the images were cropped to 79 × 79 pixels. The images were divided into training (60.42%) and test (39.58%) sets (46,349 and 30,360 images, respectively). Then, images containing chondrocytes were classified by Inception-v3 and accuracy was calculated. We visualized the images containing chondrocytes in WSIs by adding colored dots to patches. When images of chondrocytes in knee joints were evaluated, the accuracy was within the range of 91.20 ± 8.43%. Therefore, it is considered that the Inception-v3 deep learning model was able to distinguish chondrocytes from non-chondrocytes in knee joints of rats with a relatively high accuracy. The above results taken together confirmed that this deep learning model could classify the chondrocytes and this promising approach will provide pathologists a fast and accurate analysis of diverse tissue structures.

Apparent and resistant section parametric modelling of timber structures in HBIM

Historic timber structures are frequently composed by elements with irregular sections. Moreover, due to abiotic and biotic agent action, wood is especially vulnerable to decay, which can lead to destruction of external layers of the element. Due to this, a beam's resistant cross-section, which is the healthy section part that supports the structure, and the beam external apparent cross-section may not coincide. This can lead to an improper structural assessment regarding the load-capacity of that element. HBIM technology is currently being used for the representation and analysis of historic timber constructions' structural health. In this work, a methodology is proposed and applied that, based on the combination of LIDAR data with non-destructive tests, allows to obtain a single 3D HBIM model of decayed irregular wooden structures. This HBIM model contains both the apparent parametric model, that represents the structure external geometry and collects all necessary data for construction's conservation analysis, and also a parametric resistant model, that represents the beam's undamaged section, allowing to carry out a more accurate structural analysis. In this work, the methodology was first applied to an irregular and decayed beam at laboratory conditions for framework calibration, and then applied to a case study for validation. With respect to the storage space, the volume difference between the rendered models and the proposed parametric models was on average 3.2%, whereas the occupied memory space was 10 times lower in the parametric models. The case study was the Guimarães chamber roof (Portugal) which structure is made up of several truss systems composed by irregular beams, several of which with signs of past decay activity.

Determination of monopile offshore structure response to stochastic wave loads via analog filter approximation and GV-GDEE procedure

Efficient and accurate stochastic response analysis of offshore structures subjected to ocean wave loads has been a challenging problem for several decades. This is especially true for multi-degree-of-freedom nonlinear structures when information about the probability density is desired. How to account for the random field of the wave loads, and how to deal with the coupling of high-dimensional partial differential equation governing the joint probability density functions are among the major challenges. To this end, in the present paper an approach is proposed by incorporating the filter approximation of random fields of sea waves with the recently developed Globally-evolving based generalized density evolution equation (GV-GDEE). To deal with the first issue regarding the random wave filed, the analog filter technique is adopted. In particular, a filter approximation of the wave kinematics field distributed over a vertical line along the ocean domain is proposed. It comprises a linear differential equation set with white noise input. An augmented system is thereby constructed by incorporating the additional differential equation set yielding wave excitation simulated by an analog filter into the equation of motion of a monopile offshore structure. To circumvent the issue of coupling of high-dimensional equations for this augmented system with even higher dimension, the GV-GDEE approach is employed. Specifically, by constructing the effective drift coefficients based on the high-dimensional drift coefficients in the associated Fokker-Planck-Kolmogorov (FPK) equation, a two-dimensional GV-GDEE in terms of the response quantity of interest is derived. Further, this GV-GDEE is solved by the path integral method. Furthermore, two numerical examples are included. In particular, the nonstationary stochastic responses of a 5 megawatt (MW) fixed monopile offshore wind turbine structure under two operating conditions are studied. Comparisons with pertinent Monte Carlo simulations (MCS) demonstrate the accuracy and efficiency of the proposed approach.

On the microstructure and mechanical properties of FeCoCrCuNi high entropy alloy during hot compression

This study aims to find the optimized parameters such as temperature and strain rate during hot compression of FeCoCrCuNi high entropy alloy. For this purpose, hot compression tests were carried out at temperatures ranging 800–1000 °C and strain rates of 0.001–0.1 s−1. Results indicated that flow stress increases to a peak and then decreases. Also, the magnitude decreases with increasing the deformation temperature or decreasing strain rates. Moreover, accurate analysis of the dendritic structures showed that some of the dendrites were broken during hot compression and partial dynamic recrystallization occurred. To predict high temperature behavior, a constitutive equation was developed using the Zener–Hollomon parameter with a good consistency with experimental results.

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

Nonlinear static analysis of composite beams with piezoelectric actuator patches using the Refined Zigzag Theory

Piezoelectric actuators have been highly successful in a wide range of structural control applications. As such, there is an ongoing need for rapid and accurate structural analysis techniques, particularly for highly heterogeneous composite materials and accounting for the actuator as a patch.Here, a new model based on the Refined Zigzag Theory (RZT) formulation that includes geometric nonlinearities is proposed for buckling, postbuckling and nonlinear static response analyses of geometrically imperfect composite beams with piezoelectric actuators.Both the analytical and the finite element (FE) formulation are presented for symmetrically and non-symmetrically laminated beams. The FE approximation is further generalised to the case of beams with geometric discontinuities to model composite beams with piezoelectric actuator patches. The new RZT model is numerically verified through comparisons to Abaqus solutions for buckling and postbuckling analyses and for the geometrically nonlinear response to an applied voltage of geometrically imperfect composite beams with piezoelectric actuator patches.This work presents a new model for composite beams with piezoelectric actuators and confirms the remarkable advantages of RZT in terms of accuracy and computational efficiency also for challenging nonlinear analyses, where the RZT computational time is generally less than half the time required by the FE commercial code.

Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

The accurate estimations of the failure probability with low-computational burden play a vital role in structural reliability analyses. Due to high-calculation cost and time-consuming Monte Carlo simulation (MCS), this paper focused on developing a novel enhanced MCS approach with an advanced machine learning method, namely hybrid enhanced MCS (HEMCS), for achieving accurate approximation of failure probability with high-efficiency computations. The failure probability is approximated by a probabilistic model using a scalar factor less than one which is multiplied on the capacity term of performance function. An adaptive input for scalar factor is proposed by the coefficient of variation of failure probability for active region in the training process of artificial neural network (ANN) with multilayer back-propagation algorithm. Four analytical optimization training approaches, active region and number of hidden nodes are discussed for accurate approximation of ANN models. The results of the HEMCS are compared with several analytical reliability methods for numerous engineering problems. Laminated composite plate and turbine bladed disk are selected to illustrate the capability of HEMCS for approximation of failure probability. The proposed method provides higher flexibility for the prediction of failure probability when compared to the enhanced MCS which also offers significantly more accurate and computationally efficient results for high-nonlinear problems.

Accurate and efficient hydrodynamic analysis of structures with sharp edges by the Extended Finite Element Method (XFEM): 2D studies

Achieving accurate numerical results of hydrodynamic loads based on the potential-flow theory is very challenging for structures with sharp edges, due to the singular behavior of the local-flow velocities. In this paper, we introduce, perhaps the first time in the literature on marine hydrodynamics, the Extended Finite Element Method (XFEM) to solve fluid–structure interaction problems involving sharp edges on structures. Compared with the conventional FEMs, the singular basis functions are introduced in XFEM through the local construction of shape functions of the finite elements. Four different FEM solvers, including conventional linear and quadratic FEMs as well as their corresponding XFEM versions with local enrichment by singular basis functions at sharp edges, are implemented and compared. To demonstrate the accuracy and efficiency of the XFEMs, a thin flat plate in an infinite fluid domain and a forced heaving rectangle at the free surface, both in two dimensions, will be studied. For the flat plate, the mesh convergence studies are carried out for both the velocity potential in the fluid domain and the added mass, and the XFEMs show apparent advantages thanks to their local enhancement at the sharp edges. Three different enrichment strategies are also compared, and suggestions will be made for the practical implementation of the XFEM. For the forced heaving rectangle, the linear and 2nd order mean wave loads are studied. Our results confirm the previous conclusion in the literature that it is not difficult for a conventional numerical model to obtain convergent results for added mass and damping coefficients. However, when the 2nd order mean wave loads requiring the computation of velocity components are calculated via direct pressure integration, the influence of singularity is significant, and it takes a tremendously large number of elements for the conventional FEMs to get convergent results. On the contrary, the numerical results of XFEMs converge rapidly even with very coarse meshes, especially for the quadratic XFEM. Unlike other methods based on domain decomposition when dealing with singularities, the FEM framework is more flexible to include the singular functions in local approximations.