Predicted Research Articles

Comparison of modified Mallampati test and thyromental height test for preoperative airway assessment: A prospective observational study

Background: Prevision of a potentially difficult airway in the preoperative period is imperative. The available tools are evaluation of mouth opening, Mallampati test; atlanto-occipital extension; hyomental, thyromental, and sternomental distances; and upper lip bite test; thyromental height test (TMHT) is a new indicator. Aims: To compare the effectiveness of preoperative anaesthetic airway evaluation methods of TMHT and Modified mallampati test (MMT) to predict the difficulty in intubation. Materials and Methods: A prospective observational study was aimed to compare the effectiveness of preoperative anesthetic airway evaluation methods of TMHT and modified Mallampati test (MMT) to predict the difficulty in intubation. A total of 150 subjects were included in this study. MMT and TMHT were compared, and sensitivity, specificity, predictive values, and accuracy were calculated. Results: On comparison, we found TMHT to be more sensitive (93.33%) than MMT (77.78%); both tests have high specificity (TMHT 91.43%; MMT 81.90%). Positive predictive value was 82.35% for TMHT and 64.81% for MMT. Similarly, negative predictive value was 96.97% for TMHT and 89.58% for MMT. Accuracy was 92.00% for TMHT and 80.66% for MMT. Conclusions: TMHT can predict difficult intubation better than MMT (high positive predictive value). However, both TMHT and MMT predict easy intubations effectively, their negative predictive values being high.

Variability in linear polypeptide stabilizes proteoglycan than zinc finger protein in vascular smooth muscle cells: An In Silico approach

Background: Structural and physicochemical topologies of proteins play a considerable role in differentiating the functional properties of the biological system. We aimed to study the physicochemical similarities, structural and functional differences of versican (VCAN) and early growth response (Egr) proteins involved in vascular injuries. Methods: For the primary structure prediction, the proteomic tools Expasy's Protparam is used, likewise, for secondary structure and content prediction SOPM and SOPMA tool is used. The transmembrane regions in VCAN and EGR proteins are predicted through SOSUI (Classification and Secondary Structure Prediction of Membrane Proteins) server. The CYSREC tool is used to identify the presence of disulphide bonds in all the VCAN and EGR proteins, additionally through homology modelling the disulphide bonds are visualized and structure of the modelled proteins are validated through Rampage (Ramachandran plot), ProQ (Protein Quality Server) and ProSA (Protein Structure Analysis) server. Results: VCAN and Egr proteins resemble hydrophilic in nature, similarly negative score of the grand average of hydropathicity index confirms hydrophilic nature. The maximum molecular weight for VCAN is observed as 39265 and 61623 Dalton for EGR protein. VCAN proteins showed a higher level of basic residues except Q86W61, while all the Egr proteins were acidic residues. The extinction coefficient (EC) has unique absorbance at 280 nm wavelength. Based on the aliphatic index (AI ≥ 45) and instability index (II ≥ 40) most of the VCAN and Egr proteins were unstable. The Classification and Secondary Structure Prediction of Membrane Proteins server classifies all Egr and few VCAN and proteins are soluble nature. Secondary structure content prediction and SOPM server show most of the VCAN proteins are beta sheets and many Egr proteins are alpha-helical, while few with mixed structures. Besides these differences, the VCAN protein stability was identified by most probable disulfide (SS) bridges using CYS_REC tool and confirmed by homology modeling in tertiary structure. Whereas the probable disulfide bonds in Egr proteins were not identified. Conclusion: The findings with these functional and structural properties will add an extra room in understanding their dual role.

In early-stage breast cancer, sentinel lymph node biopsy can save unnecessary axillary dissection compared with fine-needle aspiration cytology for indeterminate axillary lymph nodes

Introduction The most prevalent female cancer is breast cancer. A significant prognostic factor and determinant of treatment is the state of axillary lymph nodes (LNs). Axillary LNs can be classified into pathological, indeterminate, and benign by ultrasonographic (US) assessment. Aim In early-stage breast cancer, the indeterminate axillary LNs are measured. This offers the full advantage of the available diagnostic instruments, fine-needle aspiration cytology (FNAC), and sentinel lymph node biopsy (SLNB) technique driven by US, and thus prevents excessive dissection of the axillary lymph node. In a step-wise sequence, suggestions are made for the treatment of indeterminate axillary LNs. Patients and methods This is a randomized, controlled sample that is prospective. Between October 2017 and October 2018, 60 female patients diagnosed with early-stage breast cancer with indeterminate axillary nodes were admitted to the breast cancer unit at Kasr Alainy University Hospital, Cairo University. Sensitivity, accuracy, positive predictive value, and negative predictive value of preoperative US-guided FNAC versus upfront frozen indeterminate node intraoperative SLNB were contrasted, and both findings were correlated to the final histo-guided FNAC. Results Sensitivity of SLNB was 83.33%, whereas sensitivity of the FNAC was 75%, with the negative predictive value of 83.33%, specificity of 83.3%, and positive predictive value of 75%. Conclusion FNAC is only moderately sensitive (75%), and inadequate sampling and being an operator dependent are potential limits of it. In addition, positive FNAC may cost the patient unnecessary axillary lymph node dissection.

Patient-reported shoulder morbidity and fatigue among the breast cancer survivors: An insight from a Tertiary Care Cancer Hospital

Context: Breast cancer is the most common cancer in Indian women with an annual mortality of around 87,000. Treatment for breast carcinoma may lead to swelling of the ipsilateral arm and shoulder stiffness and arm pain and cancer-related fatigue. Very few centers in India have reported the arm and shoulder morbidity treated in their hospital. Aims: The study aims to evaluate the predictive factors of arm and shoulder morbidity and fatigue among the survivors. Settings and Design: Retrospective analysis from a prospectively maintained database. Materials and Methods: Early and locally advanced cases of breast cancer patients were screened for the study during 2015–2018. Eligible participants were invited to fill up the predetermined questionnaire and their demographic and treatment-related information was accrued from a file archive. Follow-up period was estimated from the date of tissue diagnosis to last contact/time of interview. Statistical Analysis Used: Predictive factors of shoulder morbidity and fatigue were analyzed by independent sample t-test and univariate analysis. Significant predictive factors were selected for further validation in the multivariate cox regression model. A P ≤ 0.05 was considered significant. Results: Shoulder stiffness was the most common complaint followed by arm numbness. Obesity and diabetes played a crucial role in most of the morbidities and fatigue. The median fatigue score was 34 and the median time of appearance of lymphedema was 13 months. Modified radical mastectomy, and radiotherapy to axilla were significantly associated with greater shoulder stiffness and arm swelling. Conclusions: Obesity, diabetes, type of surgery, the extent of axillary dissection, and radiation plan are the major predictive factors of arm and shoulder morbidity. Further prospective validation is necessary for future breast cancer survivorship programs.

Influence of soil model complexity on the seismic response of shallow foundations

The time-history finite element analysis is usually used to evaluate the seismic response of shallow foundations. However, the literature lacks studies on the influence of the soil constitutive model complexity on the seismic response of shallow foundations. This study, thus, aims to fill this gap by investigating the seismic response of shallow foundation resting on dry silica sand using the linear elastic (LE) model, elastic-perfectly-plastic (EPP) model, and hardening soil with small strain stiffness (HS small) model. These models have been used because it is intended to compare the results of a soil constitutive model that accurately captures the seismic response of the soil-structure interaction problems (which is the HS small model) with simpler models (the LE and EPP models) that are routinely used by practitioners in geotechnical designs. The results showed that the LE model produces a very small seismic settlement value which is approximately equal to zero. The EPP model predicts a seismic settlement higher than that produced using the HS small model for earthquakes with a peak ground acceleration (PGA) lower than 0.25 g for a relative density of 45% and 0.40 g for a relative density of 70%. However, the HS small model predicts a seismic settlement higher than the EPP model beyond the aforementioned PGA values with the difference between both models increases as the PGA rises. The results also showed that the LE and EPP models predict similar trend and magnitude of the acceleration-time relationship directly below the foundation, which was different than that predicted using the HS small model. The results reported in this paper provide a useful benchmark for future numerical studies on the response of shallow foundations subjected to seismic shake.

Validation of a Prediction Model for Likelihood of Fall Prevention Actions in Community-Dwelling Older Adults: Application of the Health Belief Model

Background: It is important to understand the elderly's subjective beliefs, attitudes, and behaviors regarding fall prevention. This study used the Health Belief Model (HBM) as a theoretical framework to develop a prediction model for the likelihood of fall prevention actions in community-dwelling older adults. Methods: Subjects were 704 elderly in the communities of a county in northern Taiwan. Descriptive analysis, item analysis, factor analysis and Pearson's correlations were used for the statistical analysis. Structural equation modeling was used to verify the fit of the overall model and the amount of variance explained. Results: The results showed good overall model fit. Perceived severity (β= 0.144, p ＜ 0.01), Perceived benefit (β = 0.109, p ＜ 0.05), Self-efficacy (β= 0.408, p ＜ 0.01), and Cue to action (β= 0.212, p ＜ 0.01) can effectively predict the likelihood of the community elderly taking action to prevent falls. Self-efficacy had the largest influence, followed by Cue to action, Perceived severity, and Perceived benefit. The overall predictive power was 39.0%. Conclusion: The results provide evidence that the HBM is suitable for exploring the likelihood of fall prevention actions by community-dwelling older adults and identifying the significant influencing factors and the influence paths between factors. Understanding the impact process can help in the development of more effective interventions.

A new constitutive model to predict effective elastic properties of plain weave fabric composites

In this study, a new constitutive model has been developed to predict the elastic behavior of plain weave textile composites, using the finite element (FE) method. The geometric conditions and basic assumptions of this model are based on the basics of a continuum theory developed for the plane curved composites. In this model, the mechanical properties of the weave region and pure matrix region is calculated separately and then imported for the FE analysis. This new constitutive model is used to implement the mechanical properties of weave region in the representative volume element (RVE). The constitutive relations are implemented as user-material subroutine code (UMAT) in ABAQUS FE software. The results of FE analysis have been compared with experimental results and other data available in the literature. These comparisons confirmed the capability of the presented model for the prediction of effective elastic properties of plain weave fabric composites.

A critical appraisal of Lakshanadhyaya of Kashyapa Samhita with special reference to pediatric examination

Kashyapa Samhita is the only Ayurveda classical text, which deals exclusively with pediatric anatomy, physiology, pathology, and its management. Out of which, chapter number 28 - Lakshanadhyaya has a detailed description of anatomical and physiological features of the child and correlated it with clinical, professional, social, and spiritual fortunes. But, Lakshanadhyaya is incomplete at the end. Objective: To reveal the clinical importance of features mentioned in Lakshanadhyaya and to provide probable justification for particular future predictions given in Lakshanadhyaya. Data Source: Lakshanadhyaya of Kashyapa Samhita published by Chaukhambha Sanskrit Sansthan, Varanasi. Materials and Methods: Relevant materials were searched from sources such as published books, journals, and the Internet. A critical review was done over the same. Result: Acharya Kashyapa has predicted the quality and quantity of life by assessment of anatomical and physiological features of the child from birth till adolescence. All clinical points such as signs of gestational maturity (by examining the skin, hair, movement, and genitals), anthropometry (size of body parts), secondary sex characters (such as pubic hair, breast, and genitals), and systemic examinations (such as per vaginal, per rectal, musculoskeletal, and per abdomen examinations) are covered in Lakshanadhyaya. Conclusion: This article highlights that time to time examination of a child helps to predict their future life. The abdomen, vagina, nail, scrotum, penis, hair, joints, back, gait, etc., are important areas of examination for early detection of impending clinical manifestation.

Long-term deflection prediction in steel-concrete composite beams

This paper aims to improve the current state-of-the-art in long-term deflection prediction in steel-concrete composite beams. The efficiency of a time-dependent finite element model based on linear creep theory is verified with available experimental data. A parametric numerical study is then carried out, which focuses on the effects of concrete creep and/or shrinkage, ultimate shrinkage strain and reinforcing bars in the slab. The study shows that the long-term deformations in composite beams are dominated by concrete shrinkage and that a higher area of reinforcing bars leads to lower long-term deformations and steel stresses. The AISC model appears to overestimate the shrinkage-induced deflection. A modified ACI equation is proposed to quantify time-dependent deflections in composite beams. In particular, a modified reduction factor reflecting the influence of reinforcing bars and a coefficient reflecting the influence of ultimate shrinkage are introduced in the proposed equation. The long-term deflections predicted by this equation and the results of extensive numerical analyses are found to be in good agreement.

Investigation of performance of steel plate shear walls with partial plate-column connection (SPSW-PC)

This research endeavor intends to use the implicit finite element method to investigate the structural response of steel shear walls with partial plate-column connection. To this end, comprehensive verification studies are initially performed by comparing the numerical predictions with several reported experimental results in order to demonstrate the reliability and accuracy of the implicit analysis method. Comparison is made between the hysteresis curves, failure modes, and base shear capacities predicted numerically using ABAQUS software and obtained/observed experimentally. Following the validation of the finite element analysis approach, the effects of partial plate-column connection on the strength and stiffness performances of steel shear wall systems with different web-plate slenderness and aspect ratios under monotonic loading are investigated through a parametric study. While removal of the connection between the web-plate and columns can be beneficial by decreasing the overall system demand on the vertical boundary members, based on the results and findings of this study such detachment can lower the stiffness and strength capacities of steel shear walls by about 25%, on average.

Diagnostic value of beta-catenin immunohistochemical staining in papillary thyroid carcinoma

Background: The gold standard of diagnosing Papillary thyroid carcinoma (PTC) is achieved through pathologic evaluation using routine hematoxylin and eosin (H and E) staining. The determination of papillary carcinoma by the histological study is mainly based on architectural changes along with nuclear clearing, overlapping, grooving, and pseudoinclusions. In the absence of these changes, papillary carcinoma is difficult to distinguish from the benign thyroid lesion (BTL). Objective: This study aimed to assess the expression of β-catenin in BTL and PTC to evaluate its diagnostic values. Materials and Methods: One hundred H&E staining slides prepared from paraffin blocks (including 48 with PTC pathology, diagnosis and 52 with BTL pathology diagnosis) that met all inclusion criteria were selected. Paraffin blocks were sectioned and stained with a β-catenin marker using the immunohistochemistry method. Data were analyzed using the SPSS (V.18). Then, the sensitivity, specificity, positive predictive value, and negative predictive value were calculated to describe the diagnostic value of β-catenin. Results: Cytoplasmic and membranous expression of the β-catenin protein was observed in 98.1% and 84.6% of BTL, respectively. Related to PTC, this record was observed in 97.9% and 93.7%, respectively. The sensitivity and specificity of cytoplasmic expression were 97.9% and 1.92%, respectively. The sensitivity and specificity of membranous expression were 93.7% and 15.3%, respectively. Conclusions: β-catenin can be used as a morpho-immunohistochemical marker to detect specific structural features such as papillae and pseudoinclusion, which are not well seen in H and E staining.

Study and design of assembled CFDST column-beam connections considering column wall failure

Currently, there is a lack of research in the design approach to avoid column wall failure in the concrete filled double skin steel tubular (CFDST) column-beam connections. In this paper, a finite element model has been developed and verified by available experimental data to analyze the failure mechanism of CFDST column-beam connections. Various finite element models with different column hollow ratios () were established. The simulation result revealed that with increasing  the failure mode gradually changed from yielding of end plate, to local failure of the column wall. Detailed parametric analyses were performed to study the failure mechanism of column wall for the CFDST column-beam connection, in which the strength of sandwiched concrete and steel tube and thickness of steel tube were incorporated. An analytical model was proposed to predict the moment resistance of the assembled connection considering the failure of column wall. The simulation results indicate that the proposed analytical model can provided a conservative prediction of the moment resistance. Finally, an upper bound value of was recommend to avoid column wall failure for CFDST column-beam connections.

The Impact of the COVID-19 Pandemic on Physician-Pharmaceutical Office-Based Interactions.

BACKGROUND: COVID-19 has had significant negative economic ramifications on dermatologic care delivery, including curtailing live on-site physician-pharmaceutical-representative interactions (PPRI). OBJECTIVE: To determine the impact of COVID-19 and pandemic regulations on current and future PPRI. METHODS: Cross-sectional survey-based study that analyzed data from 400 surveyed dermatologists using a pre-validated questionnaire sent via email. Data regarding PPRI were collected over 1 week in July 2020 to compare demographics and practice standards from April 2019, April 2020, July 2020, and predictions for 2021. RESULTS: Virtual-only PPRI increased from 7.8% in April 2019 to 26.5% during April 2020 (mean difference, 18.8%; 95% confidence interval, 13.6%–23.9%). Virtual-only PPRI remained elevated at 24.5% while hybrid PPRI increased, eventually surpassing the April 2019 mark (27.0%). These trends persisted among all studied practice types and levels of experience. Practices predicted no significant percent differences in participation in PPRI (87.3% vs 90.3%; P=0.0834), but a significant shift in method of delivery where the odds ratio of incorporating a virtual component into PPRI in 2021 increased by a factor of 3. LIMITATIONS: Relatively small sample size, especially among subgroups. Responses may have been retrospective estimates. There may also be selection bias given slightly increased representation of more experienced dermatologists. CONCLUSION: PPRI materially decreased during the initial COVID-19 peak but will likely return to baseline volume moving forward with a significant component being hybrid PPRI. Further studies may better elucidate the economic and clinical impact associated with these changes and their effect on dermatologists’ ability to provide patients with samples and educational materials. J Drugs Dermatol. 2021;20(2):215-223. doi:10.36849/JDD.5651.

Steel and Composite Structures

The component-based method is widely used to analyze the initial stiffness of joint in steel structures. In this study, an analytical component model for determining the column face stiffness of square or rectangular hollow section (SHS/RHS) subjected to tension was established, focusing on endplate connections. Equations for calculating the stiffness of the SHS/RHS column face in bending were derived through regression analysis using numerical results obtained from a finite element model database. Because the presence of bolt holes decreased the bending stiffness of the column face, this effect was calculated using a novel plate-spring-based model through numerical analysis. The developed component model was first applied to predict the bending stiffness of the SHS column face determined through tests. Furthermore, this model was incorporated into the component-based method with other effective components, e.g., bolts under tension, to determine the tensile stiffness of the T-stub connections, which connects the SHS column, and the initial rotational stiffness of the joints. A comparison between the model predictions, test data, and numerical results confirms that the proposed model shows satisfactory accuracy in evaluating the bending stiffness of SHS column faces.

Landslide susceptibility assessment using feature selection-based machine learning models

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

Improving the axial compression capacity prediction of elliptical CFST columns using a hybrid ANN-IP model

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg–Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R^2 = 0.983, RMSE = 59.963 kN, a20-index = 0.979) than the ANN-LM model (R^2 = 0.938, RMSE = 116.634 kN, a20-index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

High-velocity powder compaction: An experimental investigation, modelling, and optimization

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg m3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

Crack propagation in flexural fatigue of concrete using rheological-dynamical theory

The concrete fatigue analysis can be performed with the use of fracture mechanics. The fracture mechanics defines the fatigue crack propagation as the relationship of crack growth rate and stress intensity factor. In contrast to metal, the application of fracture mechanics to concrete is more complicated and therefore many authors have introduced empirical expressions using Paris law. The topic of this paper is development of a new prediction of fatigue crack propagation for concrete using rheological-dynamical analogy (RDA) and finite element method (FEM) in the frame of linear elastic fracture mechanics (LEFM). The static and cyclic fatigue three-point bending tests on notched beams are considered. Verification of the proposed approach was performed on the test results taken from the literature. The comparison between the theoretical model and experimental results indicates that the model proposed in this paper is valid to predict the crack propagation in flexural fatigue of concrete.

Compressive strength of circular concrete filled steel tubular stubs strengthened with CFRP

The compressive strength of circular concrete filled steel tubular (C-CFST) stubs strengthened with carbon fiber reinforced polymer (CFRP) is studied theoretically. According to previous experimental results, the failure process and mechanism of circular CFRP-concrete filled steel tubular (C-CFRP-CFST) stubs is analyzed, and the loading process is divided into 3 stages, i.e., elastic stage, elasto-plastic stage and failure stage. Based on continuum mechanics, the theoretical model of C-CFRP-CFST stubs under axial compression is established based on the assumptions that steel tube and concrete are both in three-dimensional stress state and CFRP is in uniaxial tensile stress state. Equations for calculating the yield strength and the ultimate strength of C-CFRP-CFST stubs are deduced. Theoretical predictions from the presented equations are compared with existing experimental results. There are a total of 49 tested specimens, including 15 ones for comparison of yield strength and 44 ones for comparison of ultimate strength. It is found that the predicted results of most specimens are within an error limit of 10%. Finally, simplified equations for calculating both yield strength and ultimate strength of C-CFRP-CFST stubs are proposed.

Strain demand prediction of buried steel pipeline at strike-slip fault crossings: A surrogate model approach

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensionally nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.