Geometry Model Research Articles

Transfer learning for probabilistic localization of hidden cracks in concrete structures

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Numerical Modeling Reveals That Resistant Western Corn Rootworm Are Stronger Fliers than Their Susceptible Conspecifics

The hindwing geometry, aspect ratio, and numerical modeling of susceptible, Bt-Corn- and rotation-resistant western corn rootworm (WCR) wings was investigated. All variants had similar hindwing geometries and aspect ratio (AR: 6–7). These AR values correspond to wings suited to lower altitude flights of a shorter distance. These AR values are characteristic of wings that can carry heavier loads and are capable of precision flying. Numerical modeling using the finite element method (FEM) showed that the Bt-Corn-resistant and rotation-resistant WCR hindwings could potentially resist higher wind speeds with minimal deformations compared to conspecific susceptible WCR. Understanding the physiology and dispersal of resistant WCR enables a better understanding of how these variants spread their alleles across large scale agricultural landscapes. This may have important implications for integrated resistant management strategies for WCR.

Machine Learning Modeling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors

Abstract Machine learning (ML) techniques have recently gained great attention across a multitude of engineering domains, including pipeline materials. However, their application to tensile strain capacity (TSC) modeling remains unexplored. To bridge this gap, this study developed and evaluated an ML model to predict the tensile strain capacity of girth-welded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literature. The ML model demonstrated robust performance in predicting tensile strain capacities. Evidence of this lies in the near-zero means, minimal standard deviations, and normal distribution of residuals for both the training and test datasets. These collectively suggest that the model provides a good fit for the data. Furthermore, the model's loss behavior indicates successful convergence and generalization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the flaw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relatively, despite their contributions to the model. This finding underscores the importance of flaw geometry in TSC prediction models. Overall, the development of a data-driven TSC model has shown efficient TSC modeling. This model leverages ML techniques, allowing for continuous updates with new data via deep learning.

The geometry of multi-curve interest rate models

We study the problems of consistency and the existence of finite-dimensional realizations for multi-curve interest rate models of Heath–Jarrow–Morton type, generalizing the geometric approach developed by T. Björk and co-authors for the classical single-curve setting. We characterize when a multi-curve interest rate model is consistent with a given parameterized family of forward curves and spreads and when a model can be realized by a finite-dimensional state process. We illustrate the general theory in a number of model classes and examples, providing explicit constructions of finite-dimensional realizations. Based on these theoretical results, we perform the calibration of a three-curve Hull–White model to market data and analyse the stability of the estimated parameters.

Advancing packaging technology: computational fluid dynamics modeling for capillary underfill encapsulant in multi-chip heterogenous packages

Purpose The purpose of this study is to investigate the dynamics of capillary underfill flow (CUF) in flip-chip packaging, particularly in a multi-chip configuration. The study aims to understand how various parameters, such as chip-to-chip spacing (S12), chip thickness (tc) and others, affect the underfill flow process. By using computational fluid dynamics (CFD) simulations and experimental studies, the goal is to provide insights into understanding the dynamics of CUF in heterogeneous electronic packaging. Design/methodology/approach The paper introduces a CFD analysis and experimental study on CUF in a multi-chip configuration, aiming to understand underfill flow dynamics. A 3D geometry models of multi-chip arrangement are created using computer-aided design (CAD) software. After that, the CAD models are meshed and simulated in Ansys Fluent using incompressible and non-Newtonian fluid properties. The study maintains S12 of 2.86 and tc of 22.29 between experimental and simulation data for results validation. Next, a various of S12 values (1.14, 2.86, 5.71, 8.57, 14.29 and 20) which focus on tc of 22.29 have been investigated. Further studies have been conduct using S12 of 5.71 and tc of 8.00, 14.29 and 22.29. Findings Results show a strong correlation between simulation and experiment which validate the correctness and robustness of simulation. Further parameter’s studies using simulation for various of S12 indicated that higher S12 values lead to faster flow. This effect is due to large underfill weight from reservoir able to flow into S12 region which contributed to higher mass momentum movement. Furthermore, the effect of various of tc shows that the thicker the chip the faster the underfill to flow in S12 region. Research limitations/implications The intentional exclusion of solder bump pattern arrangements from the experiment and simulation may limit the study's ability to fully understand the impact of solder bump patterns on underfill flow. Therefore, more parameters can be investigated such as solder bump pattern, underfill weight and dispense pattern in the future using CFD. Practical implications The manuscript provides a comprehensive examination of the contributions of CFD to the advancement of knowledge regarding CUF phenomena in heterogeneous electronic packaging assemblies. Moreover, it delineates the utilization of CFD methodologies to assess the influence of chip-to-chip spacing (S12) and the thickness of the chip (tc) on the underfill flow characteristics. Originality/value This paper fulfills an identified need of computational fluid dynamics method to study capillary underfill flow dynamics in heterogenous electronic packaging.

Charged gravastar model in noncommutative geometry under [formula omitted] gravity

In this article, we study the properties of charged gravastars in torsion-based f(T) gravity in the presence of noncommutative geometry. We have taken the interior from noncommutative motivated space–time, noting that why, from a physical point of view, such a choice is justified, then we have taken the thin shell as stiff matter and taken three different exterior metrics (Reissner–Nordstrom (R–N), Bardeen and Ayon-Beato–Garcia (ABG) metric) to construct the gravastar model. We have studied the physical properties like proper length, entropy, energy, and EoS for these models, and we have also used Israel junction conditions to study the effective pressure, energy density, and potential of the thin shell. Finally, we comment on the stability of such a thin shell and the deflection angle caused by such a thin shell, which could, in principle, be tested by future radio telescopes like the Event Horizon Telescope (EHT).

Modeling the Geometry and Filter Composite of the Air Cleaner.

Air pollution is currently the most significant environmental factor posing a threat to the health and lives of European residents. It is a key cause of poor health, particularly respiratory and cardiovascular diseases. The primary aim of the study was to numerically determine the impact of the air purifier model's geometry on the distribution of air within a room and to conduct experimental tests on the filtration efficiency and preliminary antibacterial activity of filtration composites. The scope of the work included designing an air purifier model in the form of a pendant lamp and performing computer simulations in Ansys software to identify the optimal shape. The experimental research focused on developing filtration composites consisting of nonwoven fabric with an active hydrosol layer, meltblown nonwovens and a carbon filter. The study results showed that the SMMS composite with 50% thyme and carbon nonwoven exhibited the highest filtration efficiency for both small and large particles.

TriHuman: A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis

Creating controllable, photorealistic, and geometrically detailed digital doubles of real humans solely from video data is a key challenge in Computer Graphics and Vision, especially when real-time performance is required. Recent methods attach a neural radiance field (NeRF) to an articulated structure, e.g., a body model or a skeleton, to map points into a pose canonical space while conditioning the NeRF on the skeletal pose. These approaches typically parameterize the neural field with a multi-layer perceptron (MLP) leading to a slow runtime. To address this drawback, we propose TriHuman a novel human-tailored, deformable, and efficient tri-plane representation, which achieves real-time performance, state-of-the-art pose-controllable geometry synthesis as well as photorealistic rendering quality. At the core, we non-rigidly warp global ray samples into our undeformed tri-plane texture space, which effectively addresses the problem of global points being mapped to the same tri-plane locations. We then show how such a tri-plane feature representation can be conditioned on the skeletal motion to account for dynamic appearance and geometry changes. Our results demonstrate a clear step toward higher quality in terms of geometry and appearance modeling of humans as well as runtime performance.

Effect of food hydrocolloids on 3D meat-analog printing and deep-fat-frying

Three-dimensional (3D) printing of food product is an emerging technology. This study investigated the effects of hydrocolloid addition on 3D printing of plant-protein based meat-analogs. Meat-analog inks were formulated with soy protein isolate, gluten, canola oil, and water. Hydrocolloids (xanthan gum, pectin, hydroxypropyl methylcellulose, guar gum, locust bean gum) were added to meat-analogs formulation. The influence of hydrocolloid addition and deep-fat-frying on 3D printing process parameters, thermal, structural, and physicochemical properties of meat-analogs, were investigated. Formulated inks were used to create a specific 3D cylindrical model geometry and the printed structure were subjected to deep-fat-frying (at 180 °C, 90sec) in canola oil. Results showed that the meat-analog ink's viscosity (3871–5482 Pa s), 3D printing rate (0.34–0.39 g s−1), printing error (2.51–10.37%), printing precision (81.97–97.27%), dimensional stability (91.22–98.61%), and cooking loss (5.69–14.23%) were significantly (p < 0.05) impacted by the incorporation of hydrocolloid. Moisture-fat profile of uncooked 3D printed meat-analogs were identical, however, differences in color attributes (L∗, a∗, b∗) among the hydrocolloids added samples were observed. Moisture, fat, and color traits of 3D printed meat-analogs were substantially impacted by deep-fat-frying. During deep-fat-frying, the loss of moisture, absorption of fat, and changes in color attributes were associated with the types of hydrocolloids incorporated in formulating the meat-analog's ink. Overall, surface's structure, chemical profile, and glass-transition-temperature of 3D printed deep-fat-fried meat-analogs were extremely impacted by the addition of hydrocolloids as well as by the types of used hydrocolloids in meat-analog ink.

Geometry modeling method of a stator forming coil for permanent magnet traction motors in rail transit for manufacturing

Geometry modeling method of a stator forming coil for permanent magnet traction motors in rail transit for manufacturing

Unveiling the origin of XMM-Newton soft proton flares. I. Design and validation of a response matrix for proton spectral analysis

Low-energy ($&lt;$ 300 keV) protons entering the field of view of XMM-Newton can scatter with the X-ray mirror surface and reach the focal plane. They are observed in the form of a sudden increase in the background level, the so-called soft proton flares, affecting up to 40&lt;!PCT!&gt; of the mission observing time. Soft protons can hardly be disentangled from true X-ray events and cannot be rejected on board. All future high throughput grazing incidence X-ray telescopes operating outside the radiation belts are potentially affected by soft proton-induced contamination that must be foreseen and limited since the design phase. In-flight XMM-Newton 's observations of soft protons represent a unique laboratory to validate and improve our understanding of their interaction with the mirror, optical filters, and X-ray instruments. At the same time, such models would link the observed background flares to the primary proton population encountered by the telescope, converting XMM-Newton into a monitor for soft protons. We built a Geant4 simulation of XMM-Newton including a verified mass model of the X-ray mirror, the focal plane assembly, and the EPIC MOS and pn-CCDs. Analytical computations and, when available, laboratory measurements collected from literature were used to verify the correct modelling of the proton scattering and transmission to the detection plane. Similarly to the instrument X-ray response, we encoded the energy redistribution and proton transmission efficiency into a redistribution matrix file (RMF), mapping the probability that a proton from 2 to 300 keV is detected in a certain detector channel, and an auxiliary response file (ARF), storing the grasp towards protons. Both files were formatted according to the standard NASA calibration database and any compliant X-ray data analysis tool can be used to simulate or analyse soft proton-induced background spectra. An overall systematic uncertainty of 30&lt;!PCT!&gt; was assumed on the basis of the estimated accuracy of the mirror geometry and transmission models. For the validation, three averaged soft proton spectra, one for each filter configuration, were extracted from a collection of 13 years of MOS observations of the focused non X-ray background and analysed with Xspec . A similar power-law distribution is found for the three filter configurations, plus black-body-like emission below tens of keV used as a correction factor, based on the dedicated spectral analysis of 55 in-flight proton flares presented in Paper II. The best-fit model is in agreement with the power-law distribution predicted from independent measurements for the XMM-Newton orbit, spent mostly in the magnetosheath and nearby regions. For the first time we are able to link detected soft proton flares with the proton radiation environment in the Earth's magnetosphere, while proving the validity of the simulation chain in predicting the background of future missions. Benefiting from this work and contributions from the Athena instrument consortia, we also present the response files for the Athena mission and updated estimates for its focused charged background.

Optimizing reservoir characterization: insights from integrated data analysis

The Onshore Hydrocarbon prospectivity of the Niger Delta X-field is examined through the integration of 3D seismic and recorded information from well log data. Probable reservoirs of hydrocarbon-bearing were delineated to tackle the non-uniqueness in the identification of hydrocarbon quantity of concern. Three significant faulting patterns or systems were delineated and their architectural attributes depict a typical Niger Delta embedded anticlinal structural pattern. The study field exhibited both synthetic and antithetic structures, hence only fault on delineated reservoirs was used for structural modeling. All well locations were sited within the fault-supported synthetic and anticlinal structures and the static characteristics within the well coordinates were analyzed through petrophysical assessment. Depicted reservoir sands show low gamma ray readings, low volume of shale, considerate hydrocarbon saturation and low water saturation. The established petrophysical models showed a better net-to-gross (NTG) ratio, for the entire delineated reservoir units. This has contributed to the assessment of hydrocarbon-bearing reservoir units before employing the strategy for field development scenario, in order to eliminate dry hole drilling campaign. These will contribute to the reduction of operational costs, having delineated the accurate geometry and petrophysical model of hydrocarbon reservoir units.

A hybrid continuum-beam optimisation model: the virtual extensometer method for efficient optimisation of lattice materials

AbstractAdditive manufacturing (AM) enables the fabrication of complex lattice structures that are infeasible with traditional manufacturing processes. These structures are typically implemented with constant cross-sectional strut elements; this strategy is expedient but leads to sub-optimal structural efficiency. Numerical continuum models allow robust mechanical modelling of complex lattice geometry but provide equally complex data fields that are ill-suited for traditional optimisation techniques. By contrast, numerical beam models represent the fundamental tensile and bending stress states but are incapable of capturing nuanced geometrical effects that lead to local stress concentrations. In this research, a hybrid continuum-beam method is proposed for the systematic optimisation of strut cross section: a representative unit cell is simulated by continuum elements, and the local strut stress tensor is acquired by embedded beam elements that act as virtual extensometers. By integrating a computationally inexpensive beam model into a more robust continuum model, the volume and the quality of data returned are significantly increased whilst being provided in a readily usable format for optimisation techniques, for a trivial increase in computational cost. The presented method is generalised and can be applied to any strut-based lattice structure. Body-centred cubic (BCC) and BCC with z-strut (BCCZ) lattice structures optimised by the proposed method are fabricated using laser-based powder bed fusion (LB-PBF) in AlSi10Mg and are mechanically evaluated. On average, performance for BCC lattice structures improves by 33% and 26% for relative yield strength and relative Young’s modulus respectively. BCCZ lattice structures saw a performance improvement of 25% and 19% for relative yield strength and relative Young’s modulus respectively, thus confirming superior performance at lower relative densities when compared to incumbent designs.

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

An adaptable Digital Twin model for manufacturing

In a smart manufacturing system, physical assets are often connected to their Digital Twins in the virtual world that composes a cyber-physical production system (CPPS). Digital Twin is synchronized with the physical asset. Digital Twin is therefore considered an enabling technology of smart manufacturing. In the process of developing Digital Twins, the Digital Twin model is of great significance. Many Digital Twin models have been proposed in the past decades, including geometry models, information models, and ontology models. However, there has not been an adaptable and generic Digital Twin model that can be used for various scenarios in a manufacturing system. This paper proposes an adaptable Digital Twin model based on the object-oriented concept. The proposed Digital Twin model can be applied to different situations using object-oriented concept features. Firstly, the base Digital Twin model is developed according to ISO 23247, including the essential attributes of a Digital Twin. The aggregation and composition mechanism of the proposed Digital Twin model is also described based on the object-oriented concept. After that, according to the categories of the physical assets on the shop floor, Digital Twin models of different physical assets are developed based on the base Digital Twin model, inheriting the features of the base Digital Twin model. In the end, a case study is carried out for verification on a machine tool in the Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS).

Lithospheric Structure in the Northern Appalachian Mountains: A Detailed Examination of the Abrupt Change in Crustal Thickness in Northwestern Massachusetts

AbstractPrevious geophysical studies in the New England Appalachians identified a ∼15 km offset in crustal thickness near the surface boundary between Laurentia and the accreted terranes. Here, we investigate crustal structure using data from a denser array: New England Seismic Transects experiment, which deployed stations spaced ∼10 km apart across the Laurentia‐Moretown terrane suture in northwestern Massachusetts. We used receiver function (RF) analysis to detect P to SV converted waves and identified multiple interfaces beneath the transect. We also implemented a harmonic decomposition analysis to identify features at or near the Moho with dipping and/or anisotropic character. Beneath the Laurentian margin, the Ps converted phase from the Moho arrives almost 5.5 s after the initial P wave, whereas beneath the Appalachian terranes, the pulse arrives at 3.5 s, corresponding to ∼48 and ∼31 km depth, respectively. The character of the RF traces beneath stations in the middle of our array suggests a complex transitional zone with dipping and/or anisotropic boundaries extending at least ∼30 km. This extension is measured in our profiles and perpendicular to the suture. We propose one possible crustal geometry model that is consistent with our observations and results from previous studies.

Meshing Pair Geometry of the Intersecting-Axis Internally Geared Screw Compressor

The Intersecting-Axis Internally Geared Screw Compressor (IISC) is an emerging advanced positive displacement machine showing the potential to greatly broaden the application of screw compressor technology. The core component of the IISC is an internally geared meshing pair with the intersecting-axis gearing, while its design methodology has not been disclosed yet. To fill the gap, this paper established the geometry model for the meshing pair and discussed the influence of its core parameters on geometric performance. First, the generation model of meshing pairs was presented, and the complete parameters set of meshing pairs was defined. The established model was verified by a meshing pair manufactured by 3D printing and CNC techniques. Next, the geometric analysis model of IISCs was established such as working volume and leakage channels, which was verified by commercial 3D software. Using the analysis model, the mesh pair parameters (rotor profile, cone angle, wrap angle, and L/D ratio) were discussed to reveal the design methodology of IISCs. Finally, the variable-pitch technology was discussed, and a novel variable-pitch method was proposed to further enhance the performance of IISCs. This paper could effectively guide the design of IISCs to further promote the development of the screw compressor technology.

Impact of hydrocolloids on 3D meat analog printing and cooking

This study investigated the effects of hydrocolloid addition on three-dimensional (3D) printing and cooking of plant ingredients-based meat analog (MA). The MA inks were formulated with soy protein isolate, wheat gluten, canola oil, water, and hydrocolloids (xanthan gum, pectin, hydroxypropyl methylcellulose, guar gum, locust bean gum) at a ratio of 22:12:15:88:2. Formulated inks were used to create a specific 3D cylindrical model geometry and the printed structure were subjected to air frying (AF:180°C, 15 min) and infrared heating (IR:180°C, 15 min). Results showed that the MA ink’s viscosity (3871–5482 Pa. s), 3D printing rate (0.34–0.39 g.sec−1), printing error (2.51–10.37 %), and printing precision (81.97–97.27 %) were significantly (p<0.05) impacted by the incorporation of hydrocolloids. The dimensional stability (63.17–98.58 %), and cooking loss (6.70–17.41 %) were greatly impacted by both the hydrocolloids and post-printing cooking methods. Moisture (1.71 db) and fat (0.28 db) content of uncooked 3D printed MA were identical, whereas, differences in color attributes (L value:80.21–98.88, a value:0.01–0.13, b value:0.11–2.11) among the studied hydrocolloid added samples were observed. Moisture, fat, and color traits of 3D printed meat-analogs were significantly (p<0.05) impacted by post-printing cooking methods (AF, IR). During post-printing cooking, the loss of mass (moisture, fat) and changes in color tones were associated with the types of hydrocolloids incorporated in formulating the 3D printing ink. Surfaces and internal structure, mass loss, chemical profile, and glass-transition-temperature of 3D printed meat-analogs were significantly (p<0.05) impacted by both the type of incorporated hydrocolloids and post-printing cooking methods.

Numerical evaluation of the performance of UHPC beams under bending and shear loads using different material models

The Microplane and Menetrey-Willam concrete models are formulated on the research work of Bazant and Gambarova and Menetrey-Willam in the nineties respectively. They are used in this thesis to predict how reinforced Ultra-High-Performance Concrete (UHPC) beams will behave when subjected to flexural and shear dominant loadings. The numerical models are available in ANSYS, and they can simulate UHPC's extraordinary mechanical properties, such as high compression and tension strength, strain hardening, and softening behavior in compression and tension. In this research, four UHPC beams with dimensions of 180 wide by 270 deep by 4000 mm long with tensile reinforcements were studied using the two cementitious composite models mentioned above with the ANSYS software. The beams were investigated under flexural four-point loading and shear three-point loading with the ANSYS program. A quarter-geometric section model of the full beam was considered for the four-point loading to take advantage of symmetry in the longitudinal and transverse directions of the beam. For the three-point loading, the full geometry model of the beam was considered only because of the lack of symmetry. The results from the ANSYS finite element software were validated with an experimental study conducted by different researchers to show its capacity to predict the bending and shear behavior of reinforced UHPC beams. The comparisons of the results of both material models show that they can simulate the behavior of UHPC beams by using experimental load-deflection plots of the beam specimens and force strain plots of the longitudinal tensile reinforcements in the sample beams.