Code Structure Research Articles

NUMERICAL MODELLING OF STRUCTURAL BAMBOO UNDER COMPRESSION

Bamboo is a sustainable and eco-friendly material with great potential to replace traditionally available construction materials like concrete and steel. However, accurately predicting the behavior of bamboo is essential for improving the structural design codes, which will enhance the wider usage of bamboo as a structural member. In this study, the buckling of bamboo is modelled numerically as both cylindrical and tapered circular columns with varying elastic moduli across the cross-sections in ABAQUS. Then, the results were compared with Euler's equation derived for the cylindrical and tapered circular columns having homogeneous cross-sections. Finally, it was observed that columns with varying elastic moduli across the cross-section have 1.5 to 2.3 times a lesser buckling load capacity than columns with homogeneous cross-sections with outer Young's modulus value (Eo). Also, compared to buckling load homogeneous cross-sections with inner Young's modulus value (Ei), varying elastic modulus cross-sections have 1.7 to 2.7 times higher buckling load. This study emphasizes the necessity of having a simple analytical equation with varying elastic modulus for calculating the buckling load for bamboo columns.

NUMERICAL MODELLING OF STRUCTURAL BAMBOO UNDER COMPRESSION

Bamboo is a sustainable and eco-friendly material with great potential to replace traditionally available construction materials like concrete and steel. However, accurately predicting the behavior of bamboo is essential for improving the structural design codes, which will enhance the wider usage of bamboo as a structural member. In this study, the buckling of bamboo is modelled numerically as both cylindrical and tapered circular columns with varying elastic moduli across the cross-sections in ABAQUS. Then, the results were compared with Euler's equation derived for the cylindrical and tapered circular columns having homogeneous cross-sections. Finally, it was observed that columns with varying elastic moduli across the cross-section have 1.5 to 2.3 times a lesser buckling load capacity than columns with homogeneous cross-sections with outer Young's modulus value (Eo). Also, compared to buckling load homogeneous cross-sections with inner Young's modulus value (Ei), varying elastic modulus cross-sections have 1.7 to 2.7 times higher buckling load. This study emphasizes the necessity of having a simple analytical equation with varying elastic modulus for calculating the buckling load for bamboo columns.

Defect Prediction via Tree-Based Encoding with Hybrid Granularity for Software Sustainability

Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes' abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.

GeoTaichi: A Taichi-powered high-performance numerical simulator for multiscale geophysical problems

This study introduces GeoTaichi, an open-source high-performance numerical simulator designed for addressing multiscale geophysical problems. By leveraging the power of the Taichi parallel language, GeoTaichi maximizes the utilization of modern computer resources on multicore CPU and GPU architectures. It offers robust and reliable modules for the discrete element method (DEM), material point method (MPM), and coupled material point-discrete element method (MPDEM). These modules enable efficient solving of large-scale problems while being implemented in pure Python. The design philosophy of GeoTaichi focuses on creating a framework that is readable, extensible, and user-friendly. This paper highlights the coupling procedure of MPDEM, the code structures, and the most important features of GeoTaichi. Rigorous benchmark tests have been conducted to verify the validity and robustness of GeoTaichi. Additionally, the performance of GeoTaichi is compared with similar software tools in the field, underscoring a notable improvement in both computational efficiency and memory savings when compared to existing alternatives. Program summaryProgram title: GeoTaichiCPC Library link to program files:https://doi.org/10.17632/858bmcf7j6.1Developer's repository link:https://github.com/Yihao-Shi/GeoTaichiLicensing provisions: GNU General Public License v3.0Programming language: PythonNature of problem: The simulations of large-deformation geophysical flows and their interaction with structures play a crucial role in the field of geophysics. To address the complexities of these nonlinear problems, the discrete element method (DEM), material point method (MPM), and their coupling (MPDEM) have proven to be highly suitable numerical schemes. However, these schemes impose substantial computational demands, necessitating the development of an efficient framework that can harness modern computer resources on multicore CPU and GPU architectures.Solution method: The open-source code GeoTaichi implements the DEM, MPM, and coupled MPDEM, encompassing a range of constitutive models and contact laws for different geologic materials. The clump particle model is also introduced in DEM to solve granular mechanics involving complex-shaped particles. One significant advantage of GeoTaichi is its utilization of the Taichi parallel language, which is designed to be user-friendly and easily extensible for customized applications.

STUDY ON CONSTRUCTION MONITORING AND CONTROL OF MULTI-SPAN PRESTRESSED CONCRETE CONTINUOUS BEAM BRIDGE

This article focuses on the construction monitoring and control of a pre-stressed concrete continuous beam bridge, consisting of 13 spans. The goal is to ensure that the bridge structure meets the design requirements throughout the entire construction process. By comparing the theoretical and measured values of the bridge’s alignment and stress during the cantilever construction, closure, and completion phases, it can be observed that the deflection deformation of the bridge is generally in agreement with the theoretical calculations. After the completion of the entire bridge, the measured elevations of each section have an error range of -18mm to 20mm compared to the design elevations, which satisfies the specifications. A comparison analysis of the measured and theoretical stress values at the root and mid-span of the cantilever indicates that the stress difference at the root is within the range of -0.2MPa to 0.2MPa, and the stress differences at the mid-span after completion are 0.03MPa (upper) and 0.09MPa (lower), all of which meet the structural design and code requirements. By establishing a gray GM (1,1) model and using gray system theory, the deflection error during the construction process is predicted and controlled. The prediction accuracy of different methods is compared to determine a reasonable prediction method suitable for long-span pre-stressed continuous beam bridges, providing reference for similar engineering projects.

Influence of thermal fatigue cycles on concrete cold joints

Mass concrete structures in northern climates experience extreme oscillations in temperature each year. These fluctuating conditions incur large thermal stresses, which are of particular concern at cold joint locations. To better understand the behaviour of cold joints subjected to these thermal fatigue cycles, an experimental program was conducted at the University of Manitoba, focusing on the performance of concrete cylinders with concrete-to-concrete interfaces. Specimens were prepared with joints oriented either horizontally to evaluate direct tensile performance or at a 35°, 40°, or 45° angle for slant shear. These cylinders were then installed in specially designed vertical translation-restricting frames and placed in an environmental chamber, where they were subjected to temperatures cycling between −25 °C and 40 °C. Static testing after 50, 150, and 250 cycles was performed to ascertain remaining bond strength. An increase in performance of approximately 20 % was observed in direct tension after 50 cycles, followed by a subsequent decrease. In general, the thermal fatigue cycles did not appear to detrimentally impact bond strength after 250 cycles in both direct tension and slant shear. Additionally, interface friction demonstrated no meaningful trend between specimens that had experienced zero to 250 cycles, remaining relatively consistent with their corresponding roughness level defined in the Canadian Design of Concrete Structures code, and bond cohesion appeared to follow a similar trend to mean direct tensile bond strength results.

Bridging Gaps in Teaching and Learning Biochemistry: A Qualitative Study

Objective: To identify the sources of learning gaps in biochemistry teaching and learning and explore new teaching and learning strategies, considering the students' perceptions and felt needs. Study Design: Exploratory qualitative study (Social-constructivist philosophical worldview). Place and Duration of Study: Public Sector Medical College, Pakistan, from Jul 2020 to Jul 2021. Methodology: Students from all five years of medical college were recruited voluntarily after their written consent. Ten focus group discussions were conducted, each consisting of approximately 8 to 12 participants. Each session was recorded and then later transcribed verbatim. Verbatim FGDs were reviewed, and codes were given as the concept became recognizable. A code structure was developed via an inductive approach, and then sub-themes and themes were generated. Results: Ten major themes were generated from the FGDs. These themes were teaching, students’ motivation, clinical irrelevance, educational needs, transition difficulty, attitudes, student support, senior peers, curriculum coherence, and assessment. These themes were further elucidated to explain the causes of the learning gaps and provide suggestions. Conclusion: This study concludes that teaching, students’ motivation, clinical irrelevance, educational needs, difficulty in transition, student attitude, student support, senior peers, curriculum coherence, and assessment are the potential sources of the learning gap to achieve intended learning outcomes of biochemistry for medical students.

Stigma towards a nursing specialty: A qualitative analysis of the perceptions of the addiction nursing workforce.

Addiction nurses are highly skilled providers of holistic care and ensuring workforce sustainability is key to providing quality care to a traditionally marginalised group of healthcare consumers. The aim of this study was to explore perceived stigma towards the addiction nursing speciality, addiction nursing (also known as alcohol and other drug nursing) and its impact on workforce sustainability, retention and recruitment. Secondary analysis of qualitative interview data with nurses (n = 50) and survey data (n = 337) was conducted as part of a workforce mapping exercise in 2019. COREQ reporting guidelines were used. After structural coding was applied, three themes emerged: stigma experienced by clients of alcohol and other drug treatment services, stigma experienced by addiction nurses and a lack of awareness of the specialty of addiction nursing itself. Participants overwhelmingly felt that these forms of stigma made addiction nursing less attractive to new entrants, particularly new nurses and posed a threat to the sustainability of the specialty. The findings from this study indicate that urgent attention is required to address stigma towards individuals who use alcohol and other drugs, and the nurses providing care for them. Furthermore, creating awareness of the addiction nursing specialty is paramount to ensure workforce sustainability and to improve care for individuals who use alcohol and other drugs. Beyond addiction nurses, our results indicate that stigma towards other specialties (such as mental health nursing) is a substantive barrier to workforce sustainability.

AI-Driven Refactoring: A Pipeline for Identifying and Correcting Data Clumps in Git Repositories

Data clumps, groups of variables that repeatedly appear together across different parts of a software system, are indicative of poor code structure and can lead to potential issues such as maintenance challenges, testing complexity, and scalability concerns, among others. Addressing this, our study introduces an innovative AI-driven pipeline specifically designed for the refactoring of data clumps in software repositories. This pipeline leverages the capabilities of Large Language Models (LLM), such as ChatGPT, to automate the detection and resolution of data clumps, thereby enhancing code quality and maintainability. In developing this pipeline, we have taken into consideration the new European Union (EU)-Artificial Intelligence (AI) Act, ensuring that our pipeline complies with the latest regulatory requirements and ethical standards for use of AI in software development by outsourcing decisions to a human in the loop. Preliminary experiments utilizing ChatGPT were conducted to validate the effectiveness and efficiency of our approach. These tests demonstrate promising results in identifying and refactoring data clumps, but also the challenges using LLMs.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Design of tie-columns in confined masonry structures for lateral loads

Confined masonry (CM) buildings are generally designed with an expectation that masonry walls withstand all possible loads imparted on the buildings. The reinforced concrete (RC) confining members, also known as tie-members, have the sole purpose of constraining the masonry and avoiding its outward spread during lateral earthquake loading. Reliable design rules for tie-columns are scarce in design codes of CM structures, often resulting in the nominal design of tie-elements without considering the influence of all the important parameters related to the material and geometric properties of CM wall. This often results in the construction of weaker tie-members, especially the tie-columns, that can lead to their direct failure and the failure of the entire wall during earthquakes. To address this issue, a methodology was developed for assessing the design shear forces for the tie-columns as well as masonry subjected to lateral loading by utilizing the past observations from systematic experimental and numerical studies. The efficacy of this methodology was evaluated in the present experimental study conducted on CM walls with varying aspect ratios. The test results demonstrated that the proposed methodology can result in a significant improvement in the lateral load behavior of CM walls, by delaying the shear failure of tie-columns without jeopardizing other functional requirements.

Behaviour of concrete-filled circular steel tubular K-joints in wind turbine towers

The advantages of concrete-filled steel tubular lattice towers in stress efficiency, material consumption, and industrial construction are significant for the future development of ultra-long blades, ultra-high towers, and large-capacity units. The stress state of the joints is a key component of a wind tower structure. Therefore, experiments were conducted on four concrete-filled circular steel tubular (CFCST) K-joints in a lattice wind turbine tower, along with one hollow circular steel tubular (HCST) K-joint for comparison. The test results revealed that the CFCST K-joints failed due to local and overall buckling of the compression web, whereas no plastic deformation occurred on the chord. The stiffness of the CFCST K-joints significantly improved, and the stress concentration around the intersecting line was significantly reduced. Existing structural codes, both domestic and international, underestimate the bearing capacity of CFCST K-joints because the contribution of the core concrete cannot be accounted for. Finite element analyses indicate that the bearing capacity of the CFCST K-joints is dependent on the failure mode, primarily influenced by the thickness ratio τ and the angle θ. To prevent joint failure in the CFCST lattice wind turbine towers, specifically suggest τ ≤ 1 and θ ≤ 45°. Based on the bearing capacity calculation method proposed by CIDECT for HCST K-joints, design equations were obtained to predict the punching shear bearing capacity of CFCST K-joints using the least-squares method. The established formula exhibited high accuracy through regression verification.

Institutional pathways to psychosis for Indigenous Māori: A qualitative exploration of experiences

BackgroundInequities in the incidence and outcomes of first episode psychosis (FEP) for Indigenous peoples are impacted by multiple institutional systems. This study examines Indigenous experiences of these systems to gain an understanding of how to improve institutional responses to FEP inequity. MethodsCritical race theory informed the methods used. Twenty-three participants participated in four focus group interviews, and thirteen individual interviews, including nine Māori youth and young adults with FEP, ten family members, and four Māori health professionals. Participants were asked about lifetime experiences of institutions (health, social, and criminal-justice). An adapted WHO framework for addressing mental health inequities was used to organize and analyse the first round of structural coding, followed by descriptive and pattern coding. ResultsTwo themes were identified. The first theme signposted opportunities for institutional intervention. Participants critique being processed quickly through institutions that are driven by socio-political agendas, namely perceived risk, threat or crisis. Subsequently, opportunities for meaningful intervention were missed. The second theme identified scope for family orientated responses across sectors. Participants described short-term social interventions and institutional structures that focused on individuals rather than families, emphasizing the need to broaden cross-sector scope to structural interventions for families. DiscussionThe findings highlight institutional responses are focused on organisational drivers and downstream issues, that fail to address the fabric of the family social-environmental conditions that maintain Indigenous exposure to psychosis risk and poor outcomes. A pathway to equity would require a shared framework of social responsibility across sectors, that targets structural factors responsive to Indigenous family needs.

Joint shear strength prediction for fiber reinforced concrete beam-to-column connections

Beam-to-column joints experience substantial shear forces and deformations, playing a pivotal role in maintaining the overall stability and integrity of moment-resisting frames under earthquake loading. If fiber reinforced concrete is used instead of reinforced concrete, beam flexural failure can be achieved, which leads to a ductile behavior and increased energy dissipation capacity, when compared to joint shear failure. To ensure a ductile failure mode, accurate prediction of the joint shear strength is essential. However, there are only a limited number of analytical studies on predicting the shear strength of fiber reinforced concrete joints, most of which are only applicable to steel fibers. In this study, a comprehensive database of prior experiments conducted on fiber reinforced concrete beam-to-column connection subassemblies is compiled. Based on Pearson Correlation Analyses, the factors influencing the seismic response are determined to be the composite tensile cracking strength, axial load on the column, aspect ratios of member dimensions, and specific fiber properties such as aspect ratio and volume fraction. A joint shear strength prediction equation is then developed utilizing statistical nonlinear regression method. Validation against experimental data confirms that the proposed equation provides conservative estimates of joint shear strength and can be utilized for composites with any fiber type, even hybrid fibers; in contrast to existing equations which often lead to overestimation of strength and restricted to a specific fiber type. Moreover, the proposed equation provides engineers a practical tool to assess the seismic behavior of such connections, which has not been addressed by current structural design codes yet. The improved accuracy of the proposed equation, demonstrated by the lowest mean absolute error and coefficient of variation and the highest coefficient of correlation, positions it as a promising candidate for integration into structural design codes.

Summary of the Calculation of the Shear Capacity of Perfobond Rib Shear Connectors

The analysis of longitudinal shear resistance based on shear capacity is the basis for assessing the reliability of steel-concrete joint structures. It is of great significance for the longitudinal shear of bridge structures and even for the establishment of a shear-resistant design code for steel-concrete bridge structures based on reliability theory. In this article, a more comprehensive overview of the test methods, the current state of research, and the remaining problems in the field of connectors for perfobond rib shear connectors (PBL) are presented. Firstly, the test methods for PBL connectors are categorized. The applicable characteristics of each test method and their advantages and disadvantages are summarized, and the research progress on the calculation of shear load-capacity in this structure is discussed in detail. Finally, it is concluded that the international research on the shear capacity of PBL connectors is still at a stage where the scope of their adaptation is small. Reasonable and accurate quantification of the sensitivity parameters of the PBL connectors was studied. The normality of the test form and other relevant factors should be taken into account to establish a set of more effective shear capacity calculation methods. This can be used to guide the shear capacity design of the structure more directly.

Building Structural Design Innovation and Code Development

In recent years, Some new structural systems and design difficulties of high-rise and super high-rise buildings were emerged in Shenzhen, such as forced displacement and non-forced displacement, story lateral stiffness calculation method, frame-core wall structures with partial perimeter frame beams missing, super high-rise structure with great height-width ratio, high-rise less shear walls in one direction structure, conjoined tower structures, high-rise structure with inclined column, mega structure with belt truss, rare earthquake design method etc. The emergence and solution of these problems reflect the innovation of the structural design of high-rise buildings. Meanwhile, Structural design has encountered many difficulties and new problems. The solutions to these problems sometimes conflict with the current Chinese code, or there are no relevant provisions in the Chinese code. The current Chinese code is the basis for designing of high-rise buildings, which is the crystallization of the wisdom of previous generations, but the emergence of various new types of building structures has brought about numerous difficulties in structural design. Therefore, during the design process, it is necessary to conduct research, innovation, supplementation, and development of the current code. In view of this situation, this paper combines the previous engineering practical experience and introduces some structural design points of complex super high-rise buildings located in Shenzhen. It is for designers' reference, which also provides content for the revision of the specification.

Impact of Saudi corporate governance code and governance structures on industrial firms' performance in Saudi Arabia

This research focuses on examining how the recent Saudi Corporate Governance Code (SCGC) and internal governance structures within companies affect the performance of industrial firms listed on the Saudi Stock Exchange. The authors studied 62 industrial firms from 2012 to 2020. They analyzed data using two models to test their hypotheses, looking at firm performance through two financial indicators: return on assets (ROA) for the first model and return on equity (ROE) for the second. Both models considered the same factors: SCGC, the size and independence of the board, the size and independence of the audit committee, how often the audit committee meets, and how concentrated the ownership is. The results indicated that applying the SCGC leads to better company performance based on ROA. However, there was no noticeable impact on performance from the board or audit committee size. Likewise, having more audit committee meetings did not improve performance. On the other hand, the independence of the board and audit committee, along with ownership concentration, did have a positive effect on performance. This study adds to the discussion on the economic impacts of the SCGC in the Saudi market, offering valuable insights for companies, investors, and policymakers like the Capital Market Authority (CMA) and the Saudi Organization for Chartered and Professional Accountants (SOCPA). These insights could guide adjustments to the SCGC that better suit the unique aspects of the Saudi market.

Partially deorbitalized meta-GGA

Mejia-Rodriguez and Trickey recently proposed a procedure for removing the explicit dependence of meta-GGA exchange-correlation energy functionals Exc on the kinetic energy density τ. We present a simple modification to this approach in which the exact Kohn-Sham τ is used as input for Exc but the functional derivative of τ with respect to the density ρ, required to calculate the potential term ∫d3r′δExc∕δτ(r′)∣ρ⋅δτ(r′)∕δρ(r), is evaluated using an approximate kinetic energy density functional. This ‘half-way’ strategy ensures that the Kohn-Sham potential is a local multiplicative function (as opposed to the non-local potential of a generalized Kohn-Sham approach) while preserving the inherent non-locality of the functional itself. Electronic structure codes can be easily modified to use the new method. We validate it by quantifying the accuracy of the predicted lattice parameters, bulk moduli, magnetic moments and cohesive energies of a large set of periodic solids. An unanticipated benefit of this method is to gauge the quality of approximate kinetic energy functionals by checking if the self-consistent solution is indeed at the variational minimum.

Understanding the multi-source uncertainties effect on the seismic performance assessment of deeply hydraulic tunnels based on the generalized PDEM

One crucial element of a seismic performance evaluation approach is to appropriately account for the stochastic characteristics of SGMs and the uncertainty associated with material properties. Furthermore, the incidence angle of seismic waves may be influenced by topographic and geological factors, leading to uncertainty and randomness. This variability in incident angles has the potential to cause unforeseen structural damage. However, the current seismic design code of underground structures has commonly assumed the vertical or horizontal seismic input method in underground engineering. From the uncertain point of view, the multi-source uncertainties that incorporate the nonstationary SMGs, seismic input angles, and material parameters utilized to conduct the seismic performance assessment of HTs remain a challenge in current seismic design and performance evaluation. To overcome this challenge, the Generalized F-discrepancy method, the generalized PDEM, and the equivalent extreme-value event are introduced to conduct stochastic dynamic analysis and develop the appropriate fragility curves of HTs considering the multi-source uncertainties. The results demonstrate that the probability of damage of the HT obtained by multi-source uncertainties is significantly different in analyzing the single uncertain and two uncertainties. Moreover, it can be concluded that the multi-source uncertainties can cause more seismic demand than the single uncertain and two uncertainties under different earthquake intensity levels for the HT. In light of this, it is strongly suggested that seismic design and performance assessment of HTs take into account the relevant aspects, such as the input angles, the random features of seismic waves, and the material parameters.

Actionable code smell identification with fusion learning of metrics and semantics

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches.