Complex Path Research Articles

Engineering attributes of ground motions from February 2023 Türkiye earthquake sequence

The 2023 Türkiye earthquake sequence includes the 6 February M7.81 mainshock followed approximately 9 h later by an M7.74 event, and many smaller aftershocks including M6.81 and M6.37 events on 6 and 20 February, respectively. These events occurred in a region near the plate boundary of the East Anatolian Fault, in the proximity of which numerous ground motion recording stations had been installed north of the Türkiye–Syria border. As a result, the events were well recorded both near the fault and at rupture distances up to 582 km. We describe the available recordings and component-specific data processing performed with the aim of optimizing usable bandwidth. The resulting database includes 337, 365, 284, and 229 usable three-component recordings from the M7.81, M7.74, M6.81, and M6.37 events, respectively. We also present source, path, and site metadata that were compiled according to uniform protocols. Comparisons to a global ground motion model (GMM) for active tectonic regions and a local, Türkiye-specific model demonstrate the existence of complex path effects that result in relatively poor fits between the GMMs and observed data at large distances (generally R JB > 200 km). Under-predictions at some stations may be influenced by directivity and/or basin effects that affect the ground motions but that are not accounted for directly in the GMMs. We present analysis of spatial variability of several intensity measures. The residual maps produced from these analyses demonstrate that the global GMM overpredicts on the Anatolian block and underpredicts on the Arabian block, which is suggestive of distinct crustal attenuation features.

Experimental study on the flexural fatigue performance of slag/fly ash geopolymer concrete reinforced with modified basalt and PVA hybrid fibers

Geopolymer, owing to its inherent low-carbon emission advantages, emerges as a potential substitute to diminish reliance on high-carbon-emitting Portland cement. Nonetheless, similar to ordinary Portland cement concrete, geopolymer concrete tends to exhibit brittle fracture under cyclic bending loads. To improve its flexural fatigue performance, this paper incorporated the modified basalt (MB) fiber and PVA fiber in hybrid form into the geopolymer concrete. The flexural fatigue behavior of hybrid fibers reinforced geopolymer concrete was investigated, with a focus on the influence of varying hybrid fibers volume fractions. Digital Image Correlation technology was utilized to accurately track the evolution of crack length and the local strain field during the fatigue failure process. The results revealed that the incorporation of both MB and PVA fibers effectively delayed the initiation and propagation of cracks, leading to a more complex path of fatigue cracks. The PVA fiber demonstrated significant efficacy in inhibiting microcracks, while the MB fiber played a crucial role in controlling the expansion of macroscopic cracks. When the proportion of MB fiber was predominant in the hybrid mix, the crack development pattern mirrored that of samples containing MB fiber alone, exhibiting a greater extent of crack length. At a volume fraction of 0.9% for MB fiber and 0.2% for PVA fiber, a significant improvement in the fatigue life of the concrete was observed, resulting in a remarkable 136.23% increase compared to the control. This study has developed geopolymer concrete featuring enhanced crack resistance and superior flexural fatigue endurance under cyclic loading, promoting the application of environmentally friendly building materials in practical engineering.

Experimental investigation and validation of strength-based topology and fiber path optimization in additively manufactured continuous fiber reinforced composites

Material extrusion-based fused filament fabrication (FFF) is a widely used additive manufacturing (AM) technology that offers design freedom to fabricate intricate, robust, and lightweight parts when used with continuous carbon fiber (CCF) reinforced filaments. FFF is well-suited for optimization methods, as designers can specifically tailor the positioning of CCF filaments, albeit within certain manufacturing limitations. This study integrates experiments and finite element analysis (FEA) to investigate strength-based optimization methods applied to two benchmark structural domains and loading scenarios. Two methods were used to concurrently design the shape or topology and fiber paths: shape optimization using the level-set method, and topology optimization using both density-based solid orthotropic material with penalization (SOMP) and level-set methods. CCF filaments were printed to follow the optimized fiber paths, and a discontinuous fiber reinforced filament was used to fill in the remaining gaps. The parts were then compression molded for full consolidation, followed by testing for their stiffness and failure loads. FEA predicted locations and magnitudes of failure with reasonable accuracy, even in regions with complex stress states and fiber paths. X-ray micro-computed tomography (µCT) examination of failed coupons showed that fiber flow (deviations) during compression molding occurred and impacted parts’ performance. Finally, comparing designs optimized for stiffness versus strength showed premature failures due to stress concentrations in stiffness-optimized designs. This study underscores the critical need for integrating proper optimization strategies with realistic AM constraints and processing-induced defects to enhance the performance and reliability of CCF reinforced AM parts.

PLS multi-step regressions in data paths

Here is presented a procedure that extends standard PLS Regression to several data matrices in a path. The basic idea is to convert the path of data matrices into interconnected regressions. Forecasts by PLS are extended to multi-step forecasts for each data matrix in the path. We study how far we can make forecasts, i.e., how far we can ‘see’ in the path. It is shown how data paths are divided into parts, where multi-step forecasting can be carried out within each part. The principles of PLS are used to suggest criteria for estimation in the regressions. These methods can be used to supervise a complex path of industrial chemical/biological processes. It is shown how expanding and contracting paths, which is common for industrial processes, can be handled. These methods can be used to carry out analysis of general path models. It is shown briefly by an example how a Structural Equations Model, SEM, can be converted into a collection of sequential paths that can be analyzed by present methods. The results suggest that conclusions made at SEM analysis may not always be reliable. The theory is applied to process data. It is shown how we work with the analysis of each regression in a similar way as in PLS.

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Abstract During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

A shear‐transformation‐zone model for time‐dependent behaviours of clay

AbstractA shear‐transformation‐zone (STZ) model is proposed for time‐dependent behaviours of clay, in which the viscoplastic deformation is described by the evolution of a temperature‐like state variable. The model is featured by rate‐dependent dilatancy and a unique critical state stress ratio. In its present form, it has nine parameters, most of which can be handily calibrated, and can predict the rate‐dependent undrained shear strength, primary, and secondary creep, as well as stress relaxation for normally consolidated clay under complex stress paths using a single set of parameters. The capabilities of the model have been verified by available experimental results on five different clays.

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

Deformation of Cylindrical Shell Made of 9X2 Steel Under Complex Loading

The development of the construction industry in terms of the design and manufacture of shell structures of non-standard architectural forms made of materials with complex mechanical properties requires using modern integrated computer-aided design systems with step-by-step modeling of deformation of structural elements under operating conditions, as well as taking into account their subsequent behavior after accumulation of residual strains during plastic deformation. The purpose of the study is to simulate the process of plastic deformation of a thin-walled cylindrical shell made of 9X2 GOST 5950-2000 (Interstate Standard) steel under compression and torsion with theoretical calculations based on the general theory of elastoplastic processes by A.A. Ilyushin. The constitutive equations of the theory of elastoplastic processes by A.A. Ilyushin for complex loading path and deformation of materials in the deviatoric strain space are presented. Based on the presented solutions, according to the strain path of the 9X2 steel shell implemented in the model, the graphs showing the relation of the vector and scalar properties of the material to the arc length of the strain path are constructed. A conclusion is made about the degree of hardening of the material in question and its dependence on the magnitude of the angle of convergence at the kink point of the complex path. The graphs of changes in the constitutive plasticity functions with respect to the increments of the arc length of the strain path are presented.

Simulating worm feeding patterns with computational models

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.

Non-Integer Dimensional Analysis of Ultrasonic Wave Propagation in Fractal Porous Media

This paper explores the acoustics of porous media characterized by fractal, or self-similar, structures. Employing a fractal approach, we use differential operators in non-integer dimensional spaces to address the fundamental equations of acoustics in such media. The primary aim is to examine the transmission of ultrasonic waves within a fractal porous medium. Our findings reveal that the fractal dimension significantly influences wave transmission. In fractal porous materials, waves travel along more complex and intricate paths, resulting in increased tortuosity and attenuation. We introduce the concept of an effective path length leff , which is dependent on the fractal dimension Dx , to describe the actual trajectory of wave propagation. Additionally, we define an effective tortuosity αDx , directly proportional to leff2 , to quantify the additional tortuosity brought about by the fractal structure. The insights gained from this study are crucial, as they enhance our understanding of wave behavior in self-similar porous media, which are prevalent in various natural settings and have multiple practical applications, including sound insulation and the design of acoustic materials. Furthermore, understanding the impact of fractal dimensions on wave behavior is vital for developing more efficient acoustic solutions. This research also sets the stage for further theoretical and experimental work on applying fractal geometry to analyze wave propagation in porous structures.

Rock strength weakening subject to principal stress rotation: Experimental and numerical investigations

During the construction and operation of gas storage reservoirs, changes in the principal stress direction can induce fracture propagation under conditions of lower differential stress, potentially leading to failure in the surrounding rock. However, the weakening of strength due to pure stress rotation has not yet been investigated. Based on fracture mechanics, an enhanced Mohr-Coulomb strength criterion considering stress rotation is proposed and verified with experimental and numerical simulations. The micro-damage state and the evolution of the rock under the pure stress-rotation condition are analyzed. The findings indicate that differential stress exceeding the crack initiation stress is a prerequisite for stress rotation to promote the development of rock damage. As the differential stress increases, stress rotation is more likely to induce rock damage, leading to a transition from brittle to plastic failure, characterized by wider fractures and a more complex fracture network. Overall, a negative exponential relationship exists between the stress rotation angle required for rock failure and the differential stress. The feasibility of applying the enhanced criterion to practical engineering is discussed using monitoring data obtained from a mine-by tunnel. This study introduces new concepts for understanding the damage evolution of the surrounding rock under complex stress paths and offers a new theoretical basis for predicting the damage of gas storage reservoirs.

Analytical solution of a gradient-enhanced damage model for quasi-brittle failure

This paper presents an approach for analytically solving gradient-enhanced damage (GED) models. The proposed approach provides rigorous proofs for essential phenomena observed in the traditional GED model, including damage widening, characteristic length sensitivity, and stress-locking. The derived cohesive law is a useful technique for accurately determining the material parameters of the GED model, significantly reducing the sensitivity to characteristic length. Furthermore, this study presents an isotropic damage model that considers both tensile and shear failures and can capture complex crack paths. Finally, a series of numerical examples is shown to demonstrate the efficacy of the suggested method.

Anionic Coordination Control in Building Cu-Based Electrocatalytic Materials for CO2 Reduction Reaction.

Renewable energy-driven conversion of CO2 to value-added fuels and chemicals via electrochemical CO2 reduction reaction (CO2RR) technology is regarded as a promising strategy with substantial environmental and economic benefits to achieve carbon neutrality. Because of its sluggish kinetics and complex reaction paths, developing robust catalytic materials with exceptional selectivity to the targeted products is one of the core issues, especially for extensively concerned Cu-based materials. Manipulating Cu species by anionic coordination is identified as an effective way to improve electrocatalytic performance, in terms of modulating active sites and regulating structural reconstruction. This review elaborates on recent discoveries and progress of Cu-based CO2RR catalytic materials enhanced by anionic coordination control, regarding reaction paths, functional mechanisms, and roles of different non-metallic anions in catalysis. Finally, the review concludes with some personal insights and provides challenges and perspectives on the utilization of this strategy to build desirable electrocatalysts.

Survey on Silentinterpreter : Analysis of Lip Movement and Extracting Speech using Deep Learning

Lip reading is a complex but interesting path for the growth of speech recognition algorithms. It is the ability of deciphering spoken words by evaluating visual cues from lip movements. In this study, we suggest a unique method for lip reading that converts lip motions into textual representations by using deep neural networks. Convolutional neural networks are used in the methodology to extract visual features, recurrent neural networks are used to simulate temporal context, and the Connectionist Temporal Classification loss function is used to align lip features with corresponding phonemes. The study starts with a thorough investigation of data loading methods, which include alignment extraction and video preparation. A well selected dataset with video clips and matching phonetic alignments is presented. We select relevant face regions, convert frames to grayscale, then standardize the resulting data so that it can be fed into a neural network. The neural network architecture is presented in depth, displaying a series of bidirectional LSTM layers for temporal context understanding after 3D convolutional layers for spatial feature extraction. Careful consideration of input shapes, layer combinations, and parameter selections forms the foundation of the model's design. To train the model, we align predicted phoneme sequences with ground truth alignments using the CTC loss. Dynamic learning rate scheduling and a unique callback mechanism for training visualization of predictions are integrated into the training process. After training on a sizable dataset, the model exhibits remarkable convergence and proves its capacity to understand intricate temporal correlations. Through the use of both quantitative and qualitative evaluations, the results are thoroughly assessed. We visually check the model's lip reading abilities and assess its performance using common speech recognition criteria. It is explored how different model topologies and hyperparameters affect performance, offering guidance for future research. The trained model is tested on external video samples to show off its practical application. Its accuracy and resilience in lip-reading spoken phrases are demonstrated. By providing a deep learning framework for precise and effective speech recognition, this research adds to the rapidly changing field of lip reading devices. The results offer opportunities for additional development and implementation in various fields, such as assistive technologies, audio-visual communication systems, and human-computer interaction.

Energy-Efficient Framework for Green Cloud Computing in Dynamic Resource Allocation

Cloud Computing has been a trending technology for a few years supporting computational services over internet. But ever since its adoption, cloud’s consistent challenge is in its dynamic resource allocation. The existing cloud model details the online and offline algorithms used to decide the dynamic resource allocation. The goal is to have a dynamic resource allocation framework that aligns to cloud data management’s objective of maximizing revenue with minimum cost. This encourages both consumers and cloud providers not only with energy-efficient power usage but also high CPU utilization. This article discusses the impediments of migrating to Public Cloud, what is dynamic resource allocation, HPC workloads with complex communication path on cloud platform, and the benefits of bare metal platform for latency-sensitive applications. We shed light on trade- offs (compute balance) between Private and Public Cloud, how existing resources can be leveraged, Random forest (RF) solutions including a study on hybrid cloud computing capacity optimization framework. Understanding RL architecture, problem solving approach, learning structure and Hybrid Cloud Management Architecture framework are also explored. Also given are a few RL implemented gaming examples on how it makes an impact. Lastly, we shall do the comparisons of RL with other Machine Learning (ML) approaches.

Interface crack analysis in 2D bounded dissimilar materials using an enriched physics-informed neural networks

This study explores the application of physics-informed neural networks (PINNs) to analyze interface crack problems within the context of elastic bimaterial fracture mechanics. Bimaterial interface cracks exhibit a distinct behavior compared to cracks in homogeneous materials, and this behavior often involves oscillatory phenomena that can pose challenges in numerical modeling. By employing neural networks for solution approximation, PINNs are meshless and are trained using batches of collocation points, which may be randomly or strategically sampled across the computational domain. To effectively capture the oscillatory singular behavior in the crack-tip regions, this paper introduces an enhanced PINNs formulation that enables the modeling of interface cracks without requiring any refinement near the crack-tip. The trainable parameters of the current PINNs are dynamically optimized throughout the training process to fulfill both the underlying differential equations and the associated initial/boundary conditions. One of the significant advantages of the present PINNs is that it uses enrichment functions to capture the behavior around the crack region, allowing for greater flexibility in handling irregular or complex crack paths. The method's accuracy and stability are validated across several benchmark examples.

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Enhancing fatigue performance of AZ31 magnesium alloy components fabricated by cold metal transfer-based wire arc directed energy deposition through LPB

Cold Metal Transfer-Based Wire Arc Directed Energy Deposition (CMT-WA-DED) presents a promising avenue for the rapid fabrication of components crucial to automotive, shipbuilding, and aerospace industries. However, the susceptibility to fatigue of CMT-WA-DED-produced AZ31 Mg alloy components has impeded their widespread adoption for critical load-bearing applications. In this study, a comprehensive investigation into the fatigue behaviour of WA-DED-fabricated AZ31 Mg alloy has been carried out and compared to commercially available wrought AZ31 alloy. Our findings indicate that the as-deposited parts exhibit a lower fatigue life than wrought Mg alloy, primarily due to poor surface finish, tensile residual stress, porosity, and coarse grain microstructure inherent in the WA-DED process. Low Plasticity Burnishing (LPB) treatment is applied to mitigate these issues, which induce significant plastic deformation on the surface. This treatment resulted in a remarkable improvement of fatigue life by 42%, accompanied by a reduction in surface roughness, grain refinement and enhancement of compressive residual stress levels. Furthermore, during cyclic deformation, WA-DED specimens exhibited higher plasticity and dislocation density compared to both wrought and WA-DED + LPB specimens. A higher fraction of Low Angle Grain Boundaries (LAGBs) in WA-DED specimens contributed to multiple crack initiation sites and convoluted crack paths, ultimately leading to premature failure. In contrast, wrought and WA-DED + LPB specimens displayed a higher percentage of High Angle Grain Boundaries (HAGBs), which hindered dislocation movement and resulted in fewer crack initiation sites and less complex crack paths, thereby extending fatigue life. These findings underscore the effectiveness of LPB as a post-processing technique to enhance the fatigue performance of WA-DED-fabricated AZ31 Mg alloy components. Our study highlights the importance of LPB surface treatment on AZ31 Mg components produced by CMT-WA-DED to remove surface defects, enabling their widespread use in load-bearing applications.

A wonder adhesive for replacing the scarce metal

Introduction. Based on archival materials, the article follows a little-known fact of the Great Patriotic War 1941–1945, namely, the scientific discovery by the specialists of the Central Research Institute (TsNIPS), related to the creation of the formulation for the TsNIPS-2 construction adhesive. With its help, in a difficult period for the country, the industrial production of new construction materials was established and Red Army gunsmiths, located on the front line, quickly restored small arms that went out of action in battles with the enemy.Aim. To introduce into a scientific discourse the materials of archival sources on the history of creating a TsNIPS-2 new construction material, as well as on the individual research results of its inventor – a senior researcher, Cand. Sci. (Engineering) Maria N. Plungyanskaya, who worked for a long period in the TsNIPS Laboratory of Timber Structures.Materials and methods. Archival materials on the current topic were used in the preparation of the publication. Due to them, as well as with the help of retrospective, historical-typological and comparative research methods, the author traced certain aspects of the complex path of creating a new adhesive by clarifying its composition, order of preparation and industrial application.Results. The article studies and shows the individual stages of creating the TsNIPS adhesive formula, which ensured (due to cold-dry setting) the large-volume industrial production of high quality construction plywood of various thicknesses for the construction industry, as well as other timber structures that replaced the scarce metal. The procedure and rules for the practical use of the TsNIPS-2 adhesive by gunsmiths beyond the limits of stationary conditions (“in a trench”) for the successful restoration of wooden parts in rifles and assault rifles (butt, forearm, etc.), as well as the prompt return of thousands of weapons to the units are described.Conclusions. The content of the article will serve as a vivid illustration in the scientific and educational context about the traditions and activities of scientific schools, presented by the institutes of JSC Research Center of Construction, their contribution to the research field on the creation of new construction materials for increasing the power of the country. The research work of the Institute’s scientists was of a great practical importance, offering the construction industry new types of light industrial collapsible structures with a high transportability, their numerous achievements were effectively used at the front to achieve the Great Victory. In the period of 1930–1940, the construction, widely developed in the country, required, on the one hand, high-quality design solutions and, on the other hand, the simplicity and economy of their implementation. In these conditions, adhesive and laminated plywood structures were of a particular importance, ensuring the industrialization of the wooden construction part production and the cost-effectiveness of using timber in construction. The important properties of the TsNIPs-2 adhesive, obtained by the researcher M.N. Plungyanskaya, including water resistance and durability, as well as simplicity and ease of its handling, made it possible to use the material in industry for assembling the hulls of wooden ships, producing laminated structures, parts of cars, and oil pipelines, made of veneer or thin plywood, as well as in combat conditions for the manufacture and repair of small arms, etc., thereby bringing a significant economic effect and expanding the possibilities of its use in the domestic industry.

Path Planning Method for Manipulators Based on Improved Twin Delayed Deep Deterministic Policy Gradient and RRT*

This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted independently in path planning, resulting in a tortuous path and making it challenging to find an optimal path. The setting of reward functions in policy learning based on DRL is very complex and has poor universality, making it difficult to complete the task in complex path planning. Aiming at the insufficient exploration of the current deterministic policy gradient DRL algorithm twin delayed deep deterministic policy gradient (TD3), a stochastic policy was combined with TD3, and the performance was verified on the simulation platform. Furthermore, the improved TD3 was integrated with RRT* for performance analysis in two-dimensional (2D) and three-dimensional (3D) path planning environments. Finally, a six-degree-of-freedom manipulator was used to conduct simulation and experimental research on the manipulator.