Strength Model Research Articles

Physical Modeling of Strength and Deformation Response of Tunnel-type Anchorage used in Suspension Bridge

Physical Modeling of Strength and Deformation Response of Tunnel-type Anchorage used in Suspension Bridge

Weak and interfered self-control fails to block problematic mobile phone use: The role of craving and desire thinking

Problematic mobile phone use (PMPU) has become a worldwide phenomenon with negative impacts on adolescents’ daily lives. While self-control has been shown to be related to PMPU, little is known about the underlying mechanisms of this association. Based on the Interaction of Person-Affect-Cognition-Execution model and the strength model of self-control, the current study aims to examine the association between self-control and PMPU, to identify the indirect role of craving, and to determine whether and how the two components of desire thinking exert differential moderating effects. A sample of 1424 adolescents was recruited to complete the scales of self-control, craving, desire thinking, and PMPU. The results suggested that self-control was indirectly associated with PMPU through craving. Furthermore, this indirect association was moderated by verbal perseveration, rather than imaginal prefiguration. Specifically, the indirect association was stronger for adolescents with higher verbal perseveration. The findings deepen our understanding of how self-control is related to PMPU and distinguish the effects of two components of desire thinking among adolescents.

Investigation on dynamic splitting mechanical properties of weakly cemented siltstone based on digital image correlation method and FracPaQ algorithm

Soft strata composed of weakly cemented rock are frequently subjected to dynamic loads in underground energy mining engineering, yet the dynamic mechanical properties of these materials remain insufficiently explored. In this study, dynamic splitting tests were conducted on Brazilian disc specimens of weakly cemented siltstone to assess their dynamic mechanical and failure characteristics. The findings revealed that both dynamic strength and dissipated energy increased under higher stress rates. A dynamic splitting strength model for weakly cemented siltstone was subsequently developed based on the incubation time criterion. Additionally, the digital image correlation technology was employed to analyze strain variations during the initiation of the main crack, and the quantitative analysis of the fragmentation process indicated that average opening rates increased with rising stress rates. The FracPaQ algorithm was also applied to further characterize fracture patterns under varying stress rates, demonstrating that the Gaussian function accurately represented the relationship between deflection angles and fracture trace lengths. These research outcomes can provide essential mechanical parameters and a theoretical foundation for the safety assessment of weakly cemented rock strata in coal mining.

Electrical resistance based residual strength prediction method for SiC/SiC mini-composites after stress-free oxidation

A stress-free oxidation model considering the microstructural characteristics of SiC/SiC mini-composites was proposed based on the electrical resistance change before and after oxidation. The relationship between the electrical resistance and the thickness of the internal fiber oxide layer was established. The stress-free oxidation tests were carried out at different temperatures for different oxidation times. The results of the oxide layer thickness calculated by the model agree well with those of the SEM observation. Based on the microstructure of tensile fracture after oxidation, the residual strength model of SiC/SiC mini-composites was proposed. The residual strength of the specimens after stress-free oxidation was calculated based on the oxide layer thickness obtained from the model. The error between the calculated and experimental results is within 14.48 %.

A Prediction Model for the Unconfined Compressive Strength of Pervious Concrete Based on Mix Design and Compaction Energy Variables Using the Response Surface Methodology

Pervious concrete is desirable for water drainage in building systems, but achieving both high strength and good permeability can be challenging. Also, the importance of compaction energy is significant in determining the efficiency of pervious concrete. However, research on the development of unconfined compressive strength (UCS) prediction models for pervious concrete materials that incorporate compaction energy parameters remains unexplored. Therefore, this study aimed to balance strength and permeability while optimizing the compaction energy required for concrete production. A Central Composite Design (CCD) was used to design experiments within the response surface methodology (RSM) and evaluate the UCS, the porosity and permeability of pervious concrete specimens produced with varying cement content (280.00–340.00 kg/m3), the water-to-cement ratio (0.27–0.33), the aggregate-to-cement ratio (4:1–4.5:1), and compaction energy (represented by VeBe compaction time, 13–82 s). A regression model with goodness of fit (R2adjusted > 0.87) was calibrated to estimate the UCS of pervious concrete as a function of mix design parameters and VeBe compaction time (Tvc). This model can potentially guide field practices by recommending compaction strategies and mix designs for pervious concrete, achieving a desirable balance between mechanical strength and hydraulic permeability for building construction applications.

Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high-performance concrete (UHPC), this study examined the impact of both single factorsand combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA–SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine (SVM), and other single models, the AOA–SVR model achieves the highest R2 value and the lowest error rates. The results demonstrate that the optimized AOA–SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC.

Robust prediction models for flow and compressive strength of sustainable cement grouts for grouted macadam pavement using RSM

This study investigates the utilization of pumice stone ash in cementitious grouts for grouted macadam pavement, aiming to enhance flowability and compressive strength. Partial replacement of ordinary Portland cement (OPC) with varying percentages (5–20 %) of powdered pumice stone (PPS) ash was explored, alongside adjustments in water-to-cement (w/c) ratios. Experimental assessments, including flowability tests and compressive strength evaluations, were conducted on fresh and hardened grouts. Response surface methodology (RSM) and ANOVA analysis were employed to analyze the relationship between pumice stone ash content, w/c ratio, flow, and compressive strength. The results show that increasing the PPS content from 0 % to 20 % led to an increase in flow time from 23.6 to 34 seconds at a 0.3 w/c ratio, from 9 to 19 seconds at a 0.35 w/c ratio, and from 7 to 14 seconds at a 0.40 w/c ratio: indicating reduced flowability of the grouts. The 7-day compressive strength increased by approximately 2–17 % when 5–15 % of the cement was replaced with PPS, but a reduction occurred at 20 % PPS. Similarly, the 28-day compressive strength increased by about 4–23 % with 5–15 % PPS, while 20 % PPS led to a decrease in strength. The results highlight specific combinations of pumice stone ash content and w/c ratios, particularly 10 % PPS at 0.35 w/c and 15 % PPS at both 0.35 and 0.40 w/c, that meet grouted macadam standards, balancing flowability and strength. Fit statistics demonstrate the reliability of the predictive model. Optimization aims to maximize pumice stone ash content and w/c ratio, with graphical representation indicating an optimal composition at 17.25 % PPS and 0.35 w/c ratio for efficient grouted macadam construction. Additionally, Significant contribution towards sustainability can be achieved by replacing cement with pumice stone ash in pavement construction.

Experimental investigation on influence of embedment length, bar diameter and concrete cover on bond between reinforced bars and steel fiber reinforced concrete (SFRC)

The bond between reinforced bars and concrete has a significant impact on reinforced concrete structures. Incorporating steel fibers in concrete not only enhances the performance of the concrete but also influences the bond strength of embedded reinforcing bars. In this paper, experiments have been carried out to investigate the influences of different bar diameters, concrete cover and embedment lengths on bond-slip responses between reinforcing bars and steel fiber reinforced concrete (SFRC). Furthermore, pull-out tests with normal concrete were also executed as a comparative group. The bond-slip results of specimens were collected. The impact of embedment length, reinforcing bar diameter, and the existence of steel fibers on the bond behavior of reinforcing bars was comprehensively analyzed. Meanwhile, the results indicate that variations in the ratio of concrete cover to reinforcing bar diameter lead to different effects on the bond behavior of SFRC compared with normal concrete. Different failure modes of specimens were exhibited and discussed. Additionally, a mathematical model for bond strength of bars in SFRC was proposed based on the correction of the bond strength model with normal concrete, which shows good agreement with experimental results.

Experimental studies on the interfacial shear characteristics between joint concrete and foamed polymer in cross-river shield tunnel

The inadequate grouting performance in cross-river shield tunnels is primarily due to the insufficient bonding strength between the grouting material and the tunnel segments. In this study, a series of tests were conducted to compare foamed polymer and common grouting materials, focusing on the tangential bonding performance of the interface with tunnel segment concrete under varying humidity conditions. The applicability of polymer for treating leaks in cross-river shield tunnels was explored. The effects of polymer density, interfacial humidity, interfacial roughness, and normal stress on the shear strength of the polymer-concrete interface were investigated. A mathematical model reflecting the interface shear characteristics between polymer and concrete was established and validated. The test results have shown that, due to the fast reaction speed and high expansion rate, foamed polymer was found to be a feasible solution for addressing leakage in cross-river shield tunnels. Compared with common grouting materials, the density of foamed polymer is controllable, and the shear strength between foamed polymer and concrete segments is less affected by humid conditions. The minimum shear strength of the interface between foamed polymer and concrete is 1.2 MPa, while the maximum is 2.0 MPa. Foamed polymer can meet the needs of leakage treatment of cross-river shield tunnel. The interfacial shear strength between foamed polymer and concrete segments is directly proportional to the polymer density, interfacial roughness, and normal stress, and inversely proportional to the level of humid in the tunnel. The influence of various factors on interfacial strength is ranked as follows: polymer density > interfacial humidity > normal pressure > interfacial roughness. The residual error of linear regression mathematical model for the shear strength of interface between foamed polymer and segment concrete follows a normal distribution. The fitting results were proven to be accurate, allowing for the intuitive prediction of the quantitative relationship between shear strength and multiple influencing factors.

A flexible yield criterion for strength modeling from biaxial compression to biaxial tension

Accurate strength modeling from equi-biaxial tension (EBT) to equi-biaxial compression (EBC) is critical for the plastic behavior prediction covering the wide-range of stress triaxiality encountered in sheet metal forming. To date, however, few yield criteria are available that can precisely model the initial yield and hardening behavior under six typical stress states between EBC and EBT, simultaneously. Furthermore, there is still a lack of a unified yield criterion for accurate strength modeling across various stress state ranges. To address the issues, a theoretical framework for constructing yield criteria dependent on stress states is provided and a new analytically described isotropic yield criterion is presented in this study. The flexibility in terms of the yield locus and application range is thoroughly explored to make the new yield criterion general. Subsequently, the isotropic yield criterion is extended into an analytically described anisotropic-asymmetric yield criterion. Furthermore, the extended yield criterion is applied to capture the initial yield behavior of DP980, AA5754-O, and AZ31 sheets, and the strain hardening behavior of QP1180 sheets at various stress states ranging from EBC to EBT along different loading directions. The predicted results from the extended criterion agree well with the corresponding experimental findings. The applications demonstrate that the proposed anisotropic-asymmetric yield criterion can effectively model the initial yield and hardening behavior of HCP, BCC, and FCC metal sheets under EBT, EBC, uniaxial tension (UT), plane strain tension (PST), shear (SH), and uniaxial compression (UC) in an analytical way.

An improved dual shear unified strength model (IDSUSM) considering strain softening effect

This study proposes an improved dual shear unified strength model by introducing the plastic internal variable which reflects the collective effects of strain softening, intermediate principal stress and unequal strength under tension and compression. The improved model is then simplified into simple forms for typical stress states, including uniaxial tension and compression, plane stress pure shear and tri-axial stress states. The smooth method and conjugate gradient method are utilized to facilitate its numerical implementation, avoiding numerical singularity and non-convergence in the solution process. The physical meanings of the parameters are further clarified and their values for self-compacting concrete are determined from the results of triaxial compression tests through a combination of direct determination, equation solution and back propagation (BP) neural network optimization. Validated against the test results, the improved model gives a more accurate prediction than the traditional dual shear unified strength model and Mohr-Coulomb model, in terms of both the overall trend and representative values. Validation results show that the improved model is applicable to materials for which the compressive strength is greater than the tensile strength and the tensile strength is greater than the shear strength.

Development sustainable concrete with high-volume wastes tile ceramic: Role of silica nanoparticles amalgamation

As the global concrete industry shifts towards sustainable practices, there is an increasing focus on mitigating the environmental impacts of ordinary Portland cement (OPC)-based concrete, which is notorious for its significant greenhouse gas emissions, high energy consumption, and substantial demand for natural resources. This urgency is compounded by the growing production of waste tile ceramic materials due to rapid urban development, leading to enhanced research into their recyclability within concrete for improved durability and sustainability. This study explores the development of high-strength concrete utilizing waste tile ceramic aggregates (WTCAs) and waste tile ceramic powder (WTCP) as replacements for natural aggregate and OPC, respectively. To further enhance the concrete's mechanical properties and microstructure, silica nanoparticles derived from waste bottle glass (WBGNPs) were integrated in varying proportions ranging from 2 % to 10 % of the binder content. Optimal results were achieved with a full replacement of natural aggregate by WTCAs, 60 % substitution of OPC by WTCP, and the inclusion of 4 % WBGNPs, which collectively exhibited superior compressive, splitting tensile, and flexural strengths. Microstructural analyses revealed that the addition of WBGNPs improved the hydration process, increased gel density, and reduced porosity. From the obtained numerical results, the coefficient of determination value of 0.92 further confirms the model's predictive strength, demonstrating that the Random Forest algorithm can reliably estimate the compressive strength. The findings indicate that the concrete formulated with WTCP and WBGNPs not only meets diverse construction demands but also significantly contributes to environmental sustainability by reducing impacts on global warming and minimizing landfill usage. This study advocates for the strategic incorporation of WTCP and WBGNPs in concrete applications to promote environmentally sustainable construction practices. It is highly recommended that WTCP be utilized in sustainable binders as a replacement for OPC and natural aggregates to enhance it strength and durable properties, reduce the environmental issues, costs, and the depletion of natural resources.

Experimental Study on Mechanical Properties and Compressive Constitutive Model of Recycled Concrete under Sulfate Attack Considering the Effects of Multiple Factors

To investigate the mechanical properties and a compressive constitutive model of recycled concrete under sulfate attack considering the effects of multiple factors, two waste concrete strengths (i.e., C30 and C40), four replacement ratios of recycled coarse aggregates (i.e., 0, 30%, 50% and 100%), and two water–cement ratios (i.e., 0.50 and 0.60) were considered in this study, and a total of 32 recycled concrete specimens were designed and tested. The results indicated that the failure processes and patterns of recycled concrete were not significantly influenced by the replacement ratio of recycled coarse aggregates, the waste concrete strength, the water–cement ratio, or sulfate attack. The higher the replacement ratio of recycled coarse aggregates and the water–cement ratio and the lower the waste concrete strength, the more obvious the reduction in cubic compressive strength, with a maximum reduction of 38.48%. A prediction model for the cubic compressive strength of recycled concrete under sulfate attack was proposed. The higher the replacement ratio of recycled coarse aggregates and the water–cement ratio and the lower the waste concrete strength, the more significant the reduction in axial compressive strength, with a maximum reduction of 37.82%. A prediction model for the axial compressive strength of recycled concrete under sulfate attack was established. A compressive constitutive model of recycled concrete under sulfate attack considering the effects of the replacement ratio of recycled coarse aggregates, the waste concrete strength, and the water–cement ratio was established. The pore structure of recycled concrete was significantly destroyed by the expansion stress generated by Na2SO4 crystals: a large number of Na2SO4 crystals were attached to the surface of concrete matrix, and the concrete matrix became loose. The research results can provide a theoretical basis and data support for engineering applications of recycled concrete.

Analysis of bond strength of CFRP cables with concrete using random forest model

The use of carbon fiber-reinforced polymer (CFRP) as an alternative to steel tendons in prestressed concrete is increasingly favored due to its non-corrosive properties. A critical aspect of this substitution is the bond strength required to effectively transfer pre-stressing force. This study investigates the bond strengths of CFRP rods, CFRP strands, and steel strands following ISO 10406 standards. The research comprehensively explores the influence of cable type, cable surface roughness, and concrete strength on bond-slip behavior. Experimental results were augmented using a data augmentation method to enrich the dataset, which facilitated the development of a robust random forest (RF)-based prediction model for bond strength. The RF model achieved strong performance metrics, including a mean absolute percentage error (MAE) of 17.9 % and an R-value of 0.97, underscoring its efficacy in predicting bond strength. Notably, the model highlighted the significant impact of cable surface roughness on bond strength relative to other parameters studied based on the feature importance analysis. This study contributes to advancing the understanding of CFRP's applicability as a steel tendon replacement in prestressed concrete structures. The findings emphasize the importance of surface characteristics in optimizing bond strength, providing valuable insights for structural engineers and material scientists.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

Residual strength and critical state of strain softening–hardening considering thermal effect for brittle rocks

Since the post failure of rock has a significant effect on the performance of excavation, the residual strength of rock considering the thermal effect, is important for deep rock engineering, such as sequencing in underground excavation and tunnel support design. From the micromechanical perspective, this paper presents novel models of residual strength and critical state of strain softening–hardening, considering the thermal effect for brittle rocks. The model of the residual strength for brittle rocks was established by considering the thermal effect. Then, the criterion was proposed under for critical state of stress softening and strain hardening by considering the thermal effect. The new models include independent parameters with physical meaning. Finally, the reliability of the proposed model and criterion was verified by comparing theoretical predictions and experimental data. It was found that the proposed models can provide an effective means to predict the residual strength behavior and critical state of strain softening–hardening, considering the thermal effect for brittle rocks.

An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17–40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Exploring the Dose-Response Relationship Between Estimated Resistance Training Proximity to Failure, Strength Gain, and Muscle Hypertrophy: A Series of Meta-Regressions.

The proximity to failure in which sets are terminated has gained attention in the scientific literature as a potentially key resistance training variable. Multiple meta-analyses have directly (i.e., failure versus not to failure) or indirectly (e.g., velocity loss, alternative set structures) evaluated the effect of proximity to failure on strength and muscle hypertrophy outcomes categorically; however, the dose-response effects of proximity to failure have not been analyzed collectively in a continuous manner. To meta-analyze the aforementioned areas of relevant research, proximity to failure was quantified as the number of repetitions in reserve (RIR). Importantly, the RIR associated with each effect in the analysis was estimated on the basis of the available descriptions of the training interventions in each study. Data were extracted and a series of exploratory multilevel meta-regressions were performed for outcomes related to both strength and muscle hypertrophy. A range of sensitivity analyses were also performed. All models were adjusted for the effects of load, method of volume equating, duration of intervention, and training status. The best fit models for both strength and muscle hypertrophy outcomes demonstrated modest quality of overall fit. In all of the best-fit models for strength, the confidence intervals of the marginal slopes for estimated RIR contained a null point estimate, indicating a negligible relationship with strength gains. However, in all of the best-fit models for muscle hypertrophy, the marginal slopes for estimated RIR were negative and their confidence intervals did not contain a null point estimate, indicating that changes in muscle size increased as sets were terminated closer to failure. The dose-response relationship between proximity to failure and strength gain appears to differ from the relationship with muscle hypertrophy, with only the latter being meaningfully influenced by RIR. Strength gains were similar across a wide range of RIR, while muscle hypertrophy improves as sets are terminated closer to failure. Considering the RIR estimation procedures used, however, the exact relationship between RIR and muscle hypertrophy and strength remains unclear. Researchers and practitioners should be aware that optimal proximity to failure may differ between strength and muscle hypertrophy outcomes, but caution is warranted when interpreting the present analysis due to its exploratory nature. Future studies deliberately designed to explore the continuous nature of the dose-response effects of proximity to failure in large samples should be considered.

Optimized Gabor Filter Banks and Autoencoder Models for Enhanced Knitted Fabric Defect Detection

There is a high need for an automated system to detect fabric defects, as the current manual methods used in the garment industries in Nepal are unreliable and costly. Previous research has focused on specific fabric defects rather than overall fabric defects efficiently. This research employs two autoencoder models to identify different defects across different types of knitted fabrics, utilizing two datasets: the SFDG dataset and a custom dataset prepared from Butwal’s garment industries. The models benefit from a carefully designed Gabor filter bank to examine fabric compositions. This filter bank is fine-tuned by modifying parameters, wavelength, and orientation to detect varieties of defects in knitted fabrics. These models get feature representations from the Gabor filter bank’s outputs and help the system adapt to different types of defect patterns, making defect detection more reliable and accurate. The nearest neighbor density estimator finds possible defects and marks on the fabric images. The model's effectiveness and strength are shown by validating it on different types of knitted fabrics, including plain and patterned fabrics, using evaluation metrices like cTPR and ROC AUC. The first model achieves a cTPR of 0.879 and an AUC score of 0.947, while the second model achieves a cTPR of 0.899 and an AUC score of 0.958.

Co-evolving networks for opinion and social dynamics in agent-based models.

The rise of digital social media has strengthened the coevolution of public opinions and social interactions that shape social structures and collective outcomes in increasingly complex ways. The existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, such as echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time.