General-purpose Software Research Articles

The collapse mechanism of steel framed structure exposed to parametric design fire and travelling fire scenarios

The structural steel elements exposed to fire is susceptible to collapse owing to the temperature sensitivity of steel sections. The technical advances in the field of fire dynamics have enabled to simulate realistic fire scenarios to determine the performance of structural elements. However, a detailed parametric study is necessary to increase the knowledge on the performance of structural steel elements to various design fires. In this study, the behaviour of three-dimensional steel framed structures subjected to the action of standard fire and parametric fire as well as the travelling fire scenarios are analysed. In this work, the general-purpose finite element software ABAQUS is used to perform the analytical study. The material models calibrated with experiments were adopted for conducting parametric study. The results of the analytical study show, the uniform exposure of temperature to compartment is the most critical scenario as the compartments exposed to uniform fire is causing total collapse of structure. Uniform exposure of parametric fire to compartments is found to be less critical as the cooling phase of fire enabling more resistance against total collapse of the structure. Also, the uniform exposure of standard fire, a small resistance is observed from the unexposed columns. However, the structure exposed to parametric fire was sustained large deflections. The structure exposed to travelling fire is found to receive greater resistance before collapse. However, the structure found to collapse as the fire reaches final compartment.

Symbolic knowledge injection meets intelligent agents: QoS metrics and experiments

Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors.

Risk factors of local recurrence and distant metastasis and manifestations of toxicity during chemoradiation treatment of cervical cancer patients

Relevance. Cervical cancer remains one of the most common malignancies in women worldwide, despite declining mortality rates due to large-scale screening programs and increased use of the human papillomavirus vaccine in Western countries. Although chemoradiotherapy has been recognized as the standard therapy for patients with locally advanced carcinoma, one-third of patients experience recurrence, and the effect of its treatment is far from satisfactory. In addition, factors affecting prognosis for patients with cervical cancer remain controversial. In connection with the above, research devoted to the analysis of the long-term clinical consequences of cervical cancer treatment and the search for prognostic factors remains extremely relevant.
 The purpose of the work is to determine the risk factors of local and distant metastasis and manifestations of toxicity during chemoradiation treatment of cervical cancer patients.
 Materials and methods. Paper case histories of non-operated patients with locally disseminated сervical cancer (stages 2B–4А) who underwent special treatment in the clinic of the institute in the period 1999–2020 were selected from the archive of the State Organization «Grigoriev Institute for Medical Radiology and Oncology of the National Academy of Medical Sciences of Ukraine» for inclusion in the electronic database. The selection criterion was either a follow-up period of more than 1 year, or a tracked recurrence of the disease within a period of up to 1 year. The end of observation was considered either the death of the patient, or the appearance of oncological consequences of the disease, or withdrawal from observation for unknown reasons (censored observations). The WizWhy package of the Data Mining category was used to conduct the research and propose hypotheses, followed by hypothesis testing by non-parametric statistical methods using the STATISTICA and SPSS general-purpose software package. Non-parametric methods were used – Pearson’s chi-squared test and the Mann–Whitney U-test.
 Results and discussion. A number of parameters and factors that have predictive properties regarding the risk of the appearance of oncological consequences of treatment in the future have been identified. The main immediate and remote consequences of chemoradiation treatment of patients and remote somatic consequences of combined treatment were analyzed.
 Conclusions. Catamnetic data of «processed» case histories, provided they are processed by appropriate mathematical methods, are a potential source for obtaining new non-obvious knowledge. In particular, the correct analytical approach to the processing of formalized medical history data made it possible to find a number of risk factors for local and distant metastasis and manifestations of toxicity during chemoradiation treatment of non-operated patients with locally disseminated cervical cancer, which creates the basis for the development of proposals for adjusting antitumor therapy and individualized patient support.

Simulation of efficient Sn/Pb-based formamidinium perovskite solar cells with variation of electron transport layers

Numerical modelling on functional Sn-based perovskite solar cells (PSCs) has been performed and compared with Pb-based PSCs by using general-purpose photovoltaic device model software. The effect of variation in active layer thickness and various electron transport layers (ETLs), including tin oxide (SnO2), zinc oxide, C60, titanium dioxide, phenyl-C61- butyric acid methyl ester, on the photovoltaic parameters of Sn-based PSCs has been investigated. The active layer thickness was observed to be 500 nm, and SnO2 as ETL material resulted in the most efficient PSC. The optimized Sn-based device with formamidinium tin iodide as perovskite active layer shows promising results with a maximum power conversion efficiency of 24.41% compared to 27.49% for formamidinium lead iodide-based device. Further, other photovoltaic parameters for lead free PSC devices are quite comparable as for lead-based devices, showing the potential of Sn-based perovskite material as a fair candidate to replace toxic Pb-based-PSCs.

CuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads. Furthermore, the CUDA platform has fewer guardrails than CPU platforms that have been forced to incrementally adjust to a barrage of security attacks. Unfortunately, the peculiarities of the GPU make it difficult to directly port memory safety solutions from the CPU space. This paper presents cuCatch, a new memory safety error detection tool designed specifically for the CUDA programming model. cuCatch combines optimized compiler instrumentation with driver support to implement a novel algorithm for catching spatial and temporal memory safety errors with low performance overheads. Our experimental results on a wide set of GPU applications show that cuCatch incurs a 19% runtime slowdown on average, which is orders of magnitude faster than state-of-the-art debugging tools on GPUs. Moreover, our quantitative evaluation demonstrates cuCatch’s higher error detection coverage compared to prior memory safety tools. The combination of high error detection coverage and low runtime overheads makes cuCatch an ideal candidate for accelerating memory safety debugging for GPU applications.

PIRET-A Platform for Treatment Planning in Electroporation-Based Therapies.

We developed PIRET, a platform to predict the treatment volume in electroporation-based therapies. The platform seamlessly integrates tools to build patient-specific models where the electric field is simulated to predict the treatment volume. Patient anatomy is segmented from medical images and 3D reconstruction aids in placing the electrodes and setting up treatment parameters. Four canine patients that had been treated with high-frequency irreversible electroporation were retrospectively planned with PIRET and with a workflow commonly used in previous studies, which uses different general-purpose segmentation (3D Slicer) and modeling software (3Matic and COMSOL Multiphysics). PIRET outperformed the other workflow by 65 minutes (× 1.7 faster), thanks to the improved user experience during treatment setup and model building. Both approaches computed similarly accurate electric field distributions, with average Dice scores higher than 0.93. A platform which integrates all the required tools for electroporation treatment planning is presented. Treatment plan can be performed rapidly with minimal user interaction in a stand-alone platform. This platform is, to the best of our knowledge, the most complete software for treatment planning of irreversible electroporation. It can potentially be used for other electroporation applications.

Thermal and elastic modeling of architectural glass unevenly heated by the environment. Formal symmetry from Biot’s variational principle

The stress state resulting from an non-uniform temperature distribution in architectural glass panes, consequent to environmental actions and, in particular, to the shadows cast, is one of the main causes of failures experienced in building façades. We propose a dedicated thermo-elastic model, implemented in a finite element framework, to overcome the weakness of standardized prescriptions, the limits of simplified practical rules, and the difficulty in using general-purpose thermal softwares, not always interfaced with a structural analysis, in the common design practice.The formulation of the thermal problem is based on Biot’s variational principle for heat transfer, which facilitates the numerical implementation, via a 2D mesh in case of sharply varying temperature fields, expected for the case at hand. The elastic problem considers a kinematics à la Kirchhoff–Love for the glass plate, enriched by a term accounting for a non-linear thermal strain distribution in the thickness. The same shape functions can be used for the two problems, establishing a convenient formal analogy that facilitates the implementation. The proposed approach is here used to evaluate the stress distribution on paradigmatic case studies. The obtained results allow to recognize the main aspects that influence the state of thermal stress, a result that could be used to conceive reliable simplified methods, which may be incorporated in standards.

Design of a typical aerospace interstage structure using orthogrid/waffle configuration

The term interstage refers to a primary structure housed between two stages of a launch vehicle. It is configured to withstand the varying magnitudes of loads during the flight. In this study, an interstage cylindrical shell structure of a Launch Vehicle is designed with isogrid and orthogrid configurations, which are integral-rib-stiffened-skin configurations to have mass-optimised aerospace structures. Design based on closed form solutions, parametric optimisation and numerical analyses to validate the design are carried out for both the configurations and results are compared. Static - linear, static - nonlinear, linear eigen value buckling analysis and non-linear static riks analysis are the different kinds of numerical analyses carried out for estimating the load carrying capacity of the structural configuration under study. The objectives of this work include understanding different aerospace structural configurations, studying grided structures in depth, and using these two to model Orthogrid structures mathematically and design a typical launch vehicle interstage structure using this Orthogrid configuration. Abaqus, a general-purpose Finite Element software is used for the numerical validation. Study compares the results of both the configurations and concludes that orthogrid configuration can be designed with lesser mass for the interstage configuration, loads and kinematic conditions considered for the design problem. The least mass configurations corresponding to each configuration are obtained through parametric design iterations. Various numerical calculations are used to validate these results. The study's findings show that for the geometry, loads, and boundary conditions taken into account, orthogrid design provided superior mass optimisation than isogrid configuration.

SimpleMind: An open-source software environment that adds thinking to deep neural networks.

Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software environment for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The knowledge base can then be applied to an input image to recognize and understand its content. SimpleMind brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are dynamically chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross-checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Proof-of-principle example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI environment.

Experimental and Numerical Study of the Behavior of RC Shear Wall with Opening Using Concealed Stiffeners

A shear wall becomes weak when an opening is provided in it. It is important to provide some arrangement in the shear wall having an opening for recovering strength loss due to the opening. It may be recovered by providing some steel profiles around the opening or at weaker sections in the shear wall having an opening. At first, the identification of weaker sections in the shear wall having an opening is important and then, the wall can be made stronger as the shear wall without an opening by strengthening weaker sections. In the present study, the performance of a shear wall having an opening subjected to horizontal cyclic loading along the plane of the shear wall in the presence of concealed stiffeners is investigated. The reduced models of shear walls with openings were tested under axial and lateral load conditions. Load-carrying capacity, deformation behavior and strain behavior of shear walls were studied with experiments and the validation of the results was made with general-purpose finite element software ANSYS. Significant improvements were observed in strength, deformation and strain behavior of a shear wall having a central opening using concealed reinforced concrete (RC) stiffeners and steel tube stiffeners. KEYWORDS: Shear wall, Strength, Stiffness, Strain, Openings, Stiffeners.

Differential Elimination for Dynamical Models via Projections with Applications to Structural Identifiability

Elimination of unknowns in a system of differential equations is often required when analyzing (possibly nonlinear) dynamical systems models, where only a subset of variables are observable. One such analysis, identifiability, often relies on computing input-output relations via differential algebraic elimination. Determining identifiability, a natural prerequisite for meaningful parameter estimation, is often prohibitively expensive for medium to large systems due to the computationally expensive task of elimination. We propose an algorithm that computes a description of the set of differential-algebraic relations between the input and output variables of a dynamical system model. The resulting algorithm outperforms general-purpose software for differential elimination on a set of benchmark models from the literature. We use the designed elimination algorithm to build a new randomized algorithm for assessing structural identifiability of a parameter in a parametric model. A parameter is said to be identifiable if its value can be uniquely determined from input-output data assuming the absence of noise and sufficiently exciting inputs. Our new algorithm allows the identification of models that could not be tackled before. Our implementation is publicly available online as a Julia package.

Seismic Performance of Three Ancient Stupas in Anuradhapura, Sri Lanka

Ruwanweli, Abayagiri and Jetavana stupas situated in Anuradhapura, are three ancient wonders of Sri Lanka. Renovations and restorations of these stupas were started in early parts in the 20th century and carried out until recently. Considering their geometric proportions, cultural and social importance, there is an impending need to assess these stupas for seismic performance to avert any possible damages due to seismic activities that might occur in the future. Further, this requirement has become more important due to some researchers’ predictions on intraplate activities in the behaviour of ancient stupas with respect to possible earthquake accelerations. In this regard, two-dimensional numerical models of the three stupas were prepared using SAP2000 v.14: generalpurpose software package. Both static and dynamic analyses were performed on numerical models for self-weight and two recorded acceleration histories, respectively. Two acceleration histories are from Mahakandarawa, Sri Lanka and Manjil, Iran. Material parameters of ancient bricks were obtained from previous literature works and experimental test results conducted on similar bricks of another ancient stupa in Sri Lanka. Considering the past records of earthquakes that have occurred in Sri Lanka, it was understood that all three stupas are in the safe realm under magnitude 4.7 earthquakes (Peak Ground Acceleration of 5.48x10-5 g). Even referring to the stress distribution with magnitude 7.4 Manjil earthquake (Peak Ground Acceleration of 0.4 g), both Ruwanweli and Jetavana stupas are in safe zone.

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network and transformer based on DNN modules have been evaluated as very excellent in terms of tracking accuracy. The high computational complexity due to the usage of the DNN module is not a preferred feature in terms of execution speed, and when tracking two or more objects, a bottleneck effect occurs in the DNN accelerator such as the GPU, which inevitably results in a larger delay. To address this problem, we propose a tracker scheduling framework. First, the computation structures of representative trackers are analyzed, and the scheduling unit suitable for the execution characteristics of each tracker is derived. Based on this analysis, the decomposed workloads of trackers are multi-threaded under the control of the scheduling framework. CPU-side multi-threading leads the GPU to a work-conserving state while enabling parallel processing as much as possible even within a single GPU depending on the resource availability of the internal hardware. The proposed framework is a general-purpose system-level software solution that can be applied not only to GPUs but also to other hardware accelerators. As a result of confirmation through various experiments, when tracking two objects, the execution speed was improved by up to 55% while maintaining almost the same accuracy as the existing method.

Fully efficient, two-stage analysis of multi-environment trials with directional dominance and multi-trait genomic selection

Key messageR/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy.Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance–covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise.

Using general-purpose integer programming software to generate bounded solutions for the multiple knapsack problem: a guide for or practitioners

An NP-Hard combinatorial optimization problem that has significant industrial applications is the Multiple Knapsack Problem. If approximate solution approaches are used to solve the Multiple Knapsack Problem there are no guarantees on solution quality and exact solution approaches can be intricate and challenging to implement. This article demonstrates the iterative use of general-purpose integer programming software (Gurobi) to generate solutions for test problems that are available in the literature. Using the software package Gurobi on a standard PC, we generate in a relatively straightforward manner solutions to these problems in an average of less than a minute that are guaranteed to be within 0.16% of the optimum. This algorithm, called the Simple Sequential Increasing Tolerance (SSIT) algorithm, iteratively increases tolerances in Gurobi to generate a solution that is guaranteed to be close to the optimum in a short time. This solution strategy generates bounded solutions in a timely manner without requiring the coding of a problem-specific algorithm. This approach is attractive to management for solving industrial problems because it is both cost and time effective and guarantees the quality of the generated solutions. Finally, comparing SSIT results for 480 large multiple knapsack problem instances to results using published multiple knapsack problem algorithms demonstrates that SSIT outperforms these specialized algorithms.

Improved time-optimal static programming algorithm for hypersonic vehicle

To resolve the problem that the performance index is not optimal under the free terminal time, an improved time-optimal static programming algorithm (ITOSP) is introduced for the hypersonic vehicle. In this algorithm, the optimal initial value generator (OIVG) is used to generate optimal initial values for subsequent static programming algorithm. The improved static programming (ISP) algorithm is used to produce trajectories that satisfy the constraints with Trapezoidal integral. And the improved particle swarm optimisation (IPSO) algorithm is applied to optimise the terminal time to minimise performance index. To verify the feasibility and accuracy of the proposed method, the analysis and simulation of hypersonic vehicle static programming example for the free and fixed terminal time are carried out in three-dimensional (3D) scene. Compared to the general-purpose optimal control software (GPOPS), the proposed method has good convergence accuracy of terminal states, and the accuracy of terminal errors is improved significantly.

Gate Drive Circuit Implementation for Parallel Connection of Power Devices Considering Parasitic Inductance

SiC devices are potential future power devices because of their higher switching speed and lower ON resistance than those of Si devices. Research and development efforts to apply them in medium- and large-capacity power conversion circuits are underway. However, SiC power devices in TO packages, which are general-purpose packages, are difficult to apply in high-current applications owing to their low current ratings. Therefore, increasing the capacity of power devices by connecting them in parallel is being studied. However, the current imbalance during switching due to the differences in device characteristics and variations in parasitic inductance is problem. This study proposed a current-balancing procedure focused on the parasitic inductances around power devices and gate drive circuit implementation. The proposed method was verified by conducting double-pulse tests at 300V and 200A.

Analysis of Friction Stirs Welding Using the PROMETHEE Method

In friction stir welding (FSW) near the tool Thermo-mechanical condition of the piece at work the aim of the present work is to simulate Using general purpose software for this A fully coupled three-dimensional solid mechanical model has been created. ABAQUS/Arbitrary Lagrange- Eulerian (ALE) formulation re-meshing and large Enables transparency with distortions. Of FSW One of the least understood aspects of in friction stir welding (FSW) near the tool. For different welding speeds, forces and equipment Temperatures are recorded accurately in standard welding are done. Conditions. Numerical the simulations implemented are arbitrary Based on the Lograngian Eulerian (ALE) formula have the key aspect of the numerical approach is the palette and contact and friction between the tool accurate calculations of surface area. Accurate calculation of surface area. PROMETHEE is a priority for assessment enrichment Ranking system and its interactive help Descriptive complementary geometric analysis known as Promethee methods. In this The PROMETHEE method is the best solution from the analysis the shorter the distance and the longer the negative-ideal solution Determines the solution with the distance, but of these distances the comparison was not considered significant. Alternative: Conductivity, Thermal capacity, Density, Effusively, Emissivity. Evaluation Preference: Work piece, FSW Tool, Backing plate, strength, and conductivity. As a result FSW tool is very low As a result passive tool is very low got first rank for strength. The value of the dataset for Friction stir welding in PROMETHEE method shows that it results in strength is and top ranking.

Discrete shear projection method for mechanical buckling analysis of FGM sandwich plates

This paper deals with the mechanical buckling analysis of functionally graded material (FGM) sandwich plates using quadrilateral element Q4γs based on the discrete shear projection method (DSPM) to avoid shear locking. The element has four-node and five degrees of freedom per node. It allows modelling two types of FGM sandwich plates, i.e. type A with a homogeneous core and FGM face sheets, and type B with homogeneous top and bottom layers and an FGM core. Different thickness schemes have been considered leading to symmetrical or unsymmetrical geometrical stacking. The paper focuses on the buckling in simply supported square plates under uniaxial or biaxial loads. The linear classical buckling theory (LCBT) is retained to evaluate the critical loads. The results obtained by the Q4γs element are compared with those reported by Zenkour (2005) and using another in-house finite element based on DKMQ and DKQ elements. All results are in good agreement when the LCBT is used to define critical loads, but the paper includes selected non-linear analysis based on the general-purpose finite element software ABAQUS. The results show the limit of validity of the LCBT for unsymmetrical stacking in the sandwich of type A and all sandwich of type B.

Response Analysis of a Ballasted Rail Track Constructed on Soft Soil by 3D Modeling

The displacement, stress, and strain distributions of railway embankments on the soft deltaic deposit of the Ganges–Brahmaputra floodplain are investigated. A numerical model developed in general-purpose finite element software is used to simulate the design train load on a deltaic deposit for a 100 km/hr rail speed. The numerical analysis analogy is grounded in the spring model, where a beam under the unit load is modeled based on the Winkler foundation model concept. In the moving load simulation on soil, the static point load relating to the axle load is assigned in the form of a dynamic multiplier, determined using auxiliary software. The calculated shear force in terms of the influence line is applied as a dynamic multiplier. The numerical results demonstrate that under a dynamic train load, the loose ballast undergoes larger and more erratic displacement than the subballast. Comparative analysis between varying subballast stiffnesses shows that stiffer subballast yields smaller displacements. Moreover, a high subballast stiffness can counterbalance the potential of forming permanent deformation by generating lower strains. However, a stiffer subballast does not play a prominent role in reducing the displacement of ballast or vertical stresses. The subgrade is found to carry the maximum load, withstanding the maximum vertical stress; thus, the importance of using an improved subgrade with higher stiffness is also observed. A greater subgrade stiffness improves its load-carrying capacity but fails to reduce the tension responsible for the lateral spreading of the soft subsoil. To reduce the high radial strain, the effects of improving the stiffness properties of two immediately adjacent soft soil layers are numerically investigated. The improvement of subsoil alone is effective in reducing the radial strain, whereas the improvement of both subgrade and subsoil produces further reductions. The critical train speed generating the maximum displacement is identified as 120 km/hr, and the dynamic velocity amplitude decreases with depth. Finally, an allowable limit of rail embankment settlement on a soft deltaic deposit is observed.