Modeling Techniques Research Articles

Optimization of UHPC beams under flexural loading: A numerical and experimental investigation

Ultra-high-performance concrete is a material with enhanced mechanical properties compared to conventional concrete. These characteristics make it possible to conceive structural elements with reduced cross-sections and lower reinforcement ratios (RR) to withstand the same load capacities as conventional concrete elements. However, UHPC beams with low RRs exhibit low ductility indexes due to localization phenomena. The proposed methodology uses a genetic algorithm routine to generate an optimized cross-section to reduce UHPC consumption, decreasing the RR. The optimization methodology was based on finite element models to increase load capacity while maintaining a target minimum ductility. The experimental program tested three reference rectangular beams with different RRs and one beam with an optimized cross-section. The tested beams were then modeled using the finite element method in the Abaqus software through a modeling technique that considered the variability of the material properties by dividing the element’s volume into parts with different material properties. The results showed that changing the cross-section’s format can increase load-bearing capacity while maintaining target ductility.

Peptide Bundlemer Networks or Lattices: Controlling Cross-Linking and Self-Assembly Using Protein-like Display of Chemistry.

Coiled-coil 'bundlemer' peptides were selectively modified with allyloxycarbonyl (alloc)-protected lysine, a non-natural amino acid containing an alkene on its side chain. The specific display of this alkene from the coiled-coil surface with protein-like specificity enabled this residue to be used as a covalent linkage for creating peptide networks with controllable properties or as a physical linkage for the self-assembly of bundlemers into unexpected, intricate lattices driven by the hydrophobic nature of the side chain. For network formation, peptides were modified with both alloc-protected lysine and cysteine amino acids for solution assembly into solvent-swollen films and subsequent covalent cross-linking via thiol-ene photo click reactions. The degree of network cross-linking, as determined by rheometry, was finely tuned by varying the specific spatial display of reactive groups on the bundlemer building block particles, transitioning between intrabundle and interbundle cross-linking. The designed display of alloc groups from the center of the bundlemer building block also prompted particle self-assembly into an unexpected intricate lattice with a porous morphology. The lattices were studied in a variety of solution conditions using transmission electron microscopy, cryotransmission electron microscopy, and small-angle X-ray scattering. The approximate particle arrangement in the lattice was determined by using coarse-grained modeling and machine learning optimization techniques along with experimental methods. The proposed truss-like face-centered cubic packing of the alloc-functionalized bundlemers agrees well with the experimental results.

Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast ionospheric TEC changes linked to seismic events in Indonesia. The study focuses on two significant earthquakes: the December 2004 Sumatra earthquake and the August 2012 Sulawesi earthquake. GPS TEC data from a BAKO station near Indonesia and solar and geomagnetic data were utilized to assess the causes of TEC variations. The December 2004 Sumatra earthquake, registering a magnitude of 9.1–9.3, exhibited notable TEC variations 5 days before the event. Analysis revealed that the TEC variations were weakly linked to solar and geomagnetic activities. Both ARMA and CoK models were employed to predict TEC variations during the Earthquakes. The ARMA model demonstrated a maximum TEC prediction of 50.92 TECU and a Root Mean Square Error (RMSE) value of 6.15, while the CoK model predicted a maximum TEC of 50.68 TECU with an RMSE value of 6.14. The August 2012 Sulawesi earthquake having a magnitude of 6.6, revealed TEC anomalies 6 days before the event. For both the Sumatra and Sulawesi earthquakes, the GPS TEC variations showed weak associations with solar and geomagnetic activities but stronger correlations with the earthquake-induced electric field for the considered two stations. The ARMA model predicted a maximum TEC of 54.43 TECU with an RMSE of 3.05, while the CoK model predicted a maximum TEC of 52.90 TECU with an RMSE of 7.35. Evaluation metrics including RMSE, Mean Absolute Deviation (MAD), Relative Error, and Normalized RMSE (NRMSE) were employed to assess the accuracy and reliability of the prediction models. The results indicated that while both models captured the general trend in TEC variations, nuances emerged in their responses to seismic events. The ARMA model demonstrated heightened sensitivity to seismic disturbances, particularly evident on the day of the earthquake, whereas the CoK model exhibited more consistent performance across pre- and post-earthquake periods.

Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges

Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.

Accurate modeling and safety evaluation of dented pipeline with internal pressure based on reverse modeling technique

ABSTRACT As a common form for pipeline geometric defects, dents can induce the local stress concentration of the pipeline, which would result in the debilitation of the bearing capacity and bring huge safety hazards. In this paper, the accurate surface profiles of dented pipeline are acquired based on the 3D scanning technology and deformation detection technology, respectively. Then, the dented pipeline models are constructed by the reverse modeling technique, and the stress distribution characteristics of the dented pipeline in the operation condition are simulated using ABAQUS. The maximum von Mises stresses of a dented pipeline obtained from two proposed modeling techniques are basically consistent. However, the deformation detection technology can effectively avoid the excavation work for buried pipelines. Therefore, the reverse modeling technique based on the deformation detection technology has strong feasibility in practical projects and significant application value in the safety assessment of submarine pipelines, where excavation work is impractical.

Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations

Abstract Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

Relationship between Nature of Science Tenets and High School Students’ Acceptance of Evolutionary Theory in Cameroon

Purpose: Low levels of evolution acceptance have been reported among students in several articles using the Measure of Acceptance of the Theory of Evolution (MATE) instruments. Amongst possible causes listed in literature are students’’ religious, sociocultural background and educational approach used in teaching. Some studies have revealed that students who have a better understanding of the nature of science (NOS) are more likely to accept evolution, but the relationship between NOS and MATE has not been scientifically established. This article seeks to establish the relationship between NOS and MATE. Materials and Methods: A cross sectional survey was done using Structural Equation Modelling (SEM) techniques to investigate the extent to which NOS tenets enhance biology students’ acceptance of evolution using 482 purposeful and convenient sample size. The validity and reliability of the NOS and MATE instruments were investigated and standardised using Exploratory and Confirmatory Factor Analysis. Thereafter, a hypothesised SEM model was conceptualised and tested. Findings: The model provided a reasonable good overall fit. Only understanding of three NOS tenets namely: The empirical nature of science; use of observation and inferences in science; and the nature of scientific laws and theories has a strong positive statistically significant direct effect on acceptance of evolution. The tentativeness, subjectivity and objectivity of science, and the use of creativity and imagination in science did not significantly enhance acceptance of evolution. Implications to Theory, Practice and Policy: This can serve as a candidate theory for further investigation. It could have implication in the development of didactic strategies to overcome misconceptions in evolution.

The Role of Purchase Behavior in Consumers' Intent to Revisit and Recommend Coffee Shops in Manila: A Mediation Analysis

The study investigates the influence of purchase behaviour on customers' intentions to revisit and recommend coffee shops in Manila, utilizing a mediation analysis. In light of coffee shops' growing significance as social and recreational venues, this study attempts to comprehend the all-encompassing experience that fosters patron loyalty. The study specifically examines how characteristics such as product, service, price, and physical environment quality influence consumer satisfaction and buying behaviour. These factors then influence consumers' inclination to recommend and return. We employed a causal quantitative research methodology to gather data from 235 customers of coffee shops located in Metro Manila through a structured survey. We used a structural equation modelling technique known as partial least squares (PLS-SEM) to examine the correlations between the variables. The results indicate that the quality of the product and the pricing significantly influence customer satisfaction and purchasing behaviour, two major factors that determine intents to return and recommend. However, there was no substantial direct effect of either physical environment or service quality on purchasing behaviour, indicating the possibility of other factors at play. The study emphasizes how crucial it is to uphold fair pricing policies and excellent standards for products in order to increase client loyalty. It also emphasizes how intricate client experiences may be and how important it is for coffee shops to concentrate on the whole experience rather than just their product lines. For coffee shop owners and managers looking to improve consumer advocacy and retention in a cutthroat industry, the findings offer insightful information.

Infinite Potential of AI Chatbots: Enhancing User Experiences and Driving Business Transformation in E-commerce: Case of Palestinian E-Commerce

In the much-evolving digital age landscape, it is difficult to overlook artificial intelligence's enormous role in people. This study entails an empirical examination of the impact of AI-powered chatbots on improving customer experience, customer satisfaction, and potential benefits in the digital era. This study was handled by the use of quantitative data collection methods through an online survey from 221 online users of AI-powered chatbots in Palestine. The collected data was then analyzed using descriptive statistics and structural equation modeling techniques. According to the results, it was shown that service quality significantly influenced the intention to use chatbots by customers or users thus indirectly influencing the net benefits enjoyed by users in the digital era. Similarly, the service quality provided by AI-powered chatbots was found to significantly influence the intention to use them, thus indirectly influencing the net benefits of AI-powered chatbots. Additionally, service quality was found to influence user satisfaction with chatbots, which indirectly influenced the net benefits enjoyed by users/customers in the digital era. On the other hand, user satisfaction significantly and directly influenced the net benefits of AI-powered chatbots in the digital era. In this regard, this study significantly contributed to the AI-powered chatbot's body of knowledge in the Palestinian digital context by identifying the factors that influence the use of chatbots in the digital era from the users’ point of view with the capacity to help transform the Palestinian business landscape

Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning

The incorporation of rubber fibers (RFs) brings about significant divergence in the characteristics of rubberized concrete when contrasted with traditional varieties. Thus, raising concerns about performance under elevated temperature and prolonged exposure. This study effectively addresses the challenges of incorporating rubber fibers in concrete by using artificial neural network (ANN), gene expression programming (GEP), and bagging to examine the impact of input factors such as water-to-cement ratio (W/C), rubber fiber content (RF), elevated temperature (T), and exposure duration (t) on air-cooled compressive strength (CSA). The comprehensive literature review and advanced modeling techniques reveal that ANN excels in capturing complex relationships. In addition, GEP provides clear and accurate models through its unique approach, and Bagging enhances model stability and accuracy. These methods together offer a robust framework for estimating the CSA of rubberized concrete. Thus, contributing valuable insights for optimizing its use in construction. All three models exhibited strong performance, with the ANN emerging as the most effective choice among the evaluated models. Notably, ANN displayed the highest coefficient of determination (R2) value of 0.984, indicating its superior predictive accuracy compared to both GEP (0.982), and bagging (0.970). Moreover, ANN demonstrated the lowest mean absolute error (MAE) score of 0.621 and root mean square error (RMSE) of 0.867, underscoring its precision in forecasting the CSA of rubberized concrete with minimal deviation from experimental values. In addition, the SHapley Additive exPlaination (SHAP) method is employed to comprehend the model estimations. The ICE and PDP plots demonstrate an initial increase in CSA up to 150°C, followed by a significant decrease as temperature rises. Furthermore, CSA decreases with higher RF contents, and linearly declines with increasing W/C ratio. The SHAP analysis provides clear evidence of the strong negative correlation between T and CSA, along with a negative association with RF. A graphical user interface has been developed to estimate the CSA of rubberized concrete, enabling efficient and user-friendly model interaction without the need for physical experimentation.

Use of Historical Road Incident Data for the Assessment of Road Redesign Potential

Drivers’ safety and overall road functionality are key triggers for deciding on road interventions. Because of the socioeconomical implications of traffic incidents, either fatal or no, continuous research has been dedicated over the previous decades on the assessment of factors contributing to crash potential. Apart from the behavioral aspects of driving, which are commonly studied through simulation and advanced modelling techniques, the road infrastructure status is of equal or even higher significance. In this study, an approach is presented to discuss the road redesign potentials based on the evaluation of network-level historical incident records from road crashes in Greece. Based on total and fatal crash records, the following infrastructure-related aspects were assessed as critical for the discussion of the road redesign potential needs: the status of road’s surface (i.e., dry, wet, etc.), the issue of improving driving conditions near at-grade intersections, the presence and suitability of signage and/or lighting, and the consideration of particular geometric design features. Overall, it is deemed that intervention actions for at least one of these pillars should aim at enhancing the safety and functionality of roadways.

A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is essential for tackling urban environmental challenges and facilitating renewable energy recycling. The MSWI process has characteristics of multiple variables, strong coupling, and complex nonlinearity, requiring advanced process control (APC) technology. Although there have been several reviews on the modeling and control of the MSWI process, there is a lack of focus on model predictive control (MPC), a widely used APC technology. This article aims to comprehensively review MPC strategies in the MSWI process. First, it describes MSWI process technology in detail, examining control issues and objectives to highlight the complexity and challenges in controller design while providing an overview of MPC methods and their benefits. Second, it reviews incinerator modeling for control, including traditional modeling techniques and machine learning technologies such as fuzzy neural networks. Third, it reviews the controllers used for MSWI process, emphasizing the advantages of MPC over existing control methods. Fourth, it discusses the current status of MPC design and online updates, covering the need for an accurate dynamic predictive model and objective function and the online updates components such as predictive modeling, rolling optimization, and feedback correction. Finally, the study concludes with a summary of the findings.

Assessment of the synergy degree of China's food safety risk governance policy tools based on text mining and machine learning methods

SummaryImplementing proactive and effective policy tools is essential for governments to manage food safety risks. Existing studies have overlooked the synergistic effects among policy tools for governing food safety risks in China. This study uses policy tool synergy to develop a quantitative model to evaluate the degree of synergy among policy tools for food safety risk governance by considering its vertical, horizontal, and temporal dimensions. Using the Top2Vec topic model and text mining techniques, we examined 558 policy documents implemented by the central and six provincial governments. The findings reveal that China's food safety risk governance system has established a collaborative mechanism, with the Administration for Market Regulation, the Department of Agriculture and Rural Affairs, and the Health Commission playing crucial roles in policy implementation. Vertical and horizontal synergies between the upper and lower levels of government and among departments at the same level are crucial for enhancing comprehensive synergy.

Hybrid toughening effect of flax fiber and thermoplastic polyurethane elastomer in 3D‐printed polylactic acid composites

AbstractFlax fiber has emerged as a promising, eco‐friendly alternative to traditional synthetic reinforcement in polymer composites. However, manufacturing biocomposites using three‐dimensional (3D) printing technology is typically accompanied by significant processing challenges and weak product performance under dynamic loading conditions. This study aims to unlock the potential of 3D‐printed polylactic acid (PLA) by incorporating chemically modified chopped flax fibers and thermoplastic polyurethane elastomer to improve impact strength and processability. To achieve this, we employed the fused deposition modeling (FDM) technique to prepare composite specimens for the study. The crystallization behavior, tensile and impact properties, as well as the fracture behavior of the composites were investigated. The findings suggest that our approach stands out because it not only facilitates the challenging task of 3D printing PLA with fiber additives of high weight fraction and high aspect ratio but also results in a remarkable 120% enhancement in impact strength and an around 31.2% increase in tensile elongation compared to neat PLA, without compromising the elastic modulus.Highlights Flax fibers were modified through alkalization and silanization. Alkalization significantly enhanced printing quality. Silanization reduced fiber attrition and doubled the fiber aspect ratio. TPU particles facilitated the 3D printing of biocomposites. For the first time, the hybrid strategy doubled the impact strength of PLA.

Road transportation emission prediction and policy formulation: Machine learning model analysis

Minimizing the detrimental effects of road transport greenhouse gas (GHG) emissions on climate change and global warming requires accurate emission forecasting. To forecast greenhouse gas emissions from industrial and civilian transportation on roads in China, we present new approaches that use data extraction and managed machine learning methods for regression and identification. Four methods are examined: decision tree, multinomial logistic regression, multivariate linear regression, and artificial neural network (ANN) multiple-layer perceptron. The findings suggest that the multiple-layer perceptron approach of ANN has superior prediction accuracy compared to other models. Ensemble modelling techniques, such as Bagging and Boosting, significantly improved the predictive performance of the developed multilayer perceptron system. The paper’s conclusions are significant for transport policymakers, regulators, and international organizations in mitigating GHG emissions.

Accelerating development in wood‐plastic composites: Presenting key material properties and demonstrating statistical methods

AbstractIn a time when global initiatives to lower CO2 emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development.Highlights Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach.

Trends in Malaysian Adolescents’ Mathematics Performance Across Two Decades: What Factors Matter?

The relationship between individual and school factors plays a crucial role in shaping adolescents' mathematics achievement. Individual factors, such as a student's motivation and educational expectation, directly influence their engagement and persistence in mathematical learning. Concurrently, school factors, including teacher quality and the availability of resources, create an environment that either fosters or hinders students' academic success in mathematics. Together, these factors underscore the need for a holistic approach to education that recognizes the interconnectedness of personal and institutional influences on student performance. Thus, this study investigates what individual and school factors have been associated with adolescents’ mathematics achievement over the last two decades. We examined six-wave data of Malaysia from the Trends in International Mathematics and Science Study (TIMSS). Multilevel modeling technique was employed for data composed of 5557, 5314, 4466, 5733, 9726, and 7065 eighth-grade students participating in 1999, 2003, 2007, 2011, 2015, and 2019, respectively. The results showed that 65.17%, 57.44%, 65.89%, 73.00%, 43.16%, and 61.69% of the total variance in mathematics achievement was due to school differences in these years. The findings also indicated that students with higher mathematics motivational beliefs scored higher. Students who more frequently spoke the language of the test at home achieved lower scores in the four waves of TIMSS; however, this trend changed in 2015 and 2019. Student educational aspiration emerged as a positive predictor of mathematics achievement in the last two assessments of TIMSS. Home educational resources and book ownership also significantly correlated with mathematics achievement. At the school level, teacher gender was negatively associated with achievement in all TIMSS waves except 2019. Students whom female teachers taught outperformed those whom male teachers taught. The longitudinal study of Malaysian students' mathematics performance reveals critical insights for educational stakeholders. The findings indicate that 43.16% to 73.00% of the variance in mathematical achievement is linked to school differences, underscoring the importance of equitable resource distribution. Moreover, students' motivational beliefs significantly influence their mathematics scores, suggesting a need for interventions that foster positive attitudes. The evolving correlation between home language proficiency and achievement emphasizes the necessity of integrating language support into educational strategies. Additionally, promoting educational aspirations is vital for enhancing performance, while the role of gender dynamics in teacher effectiveness warrants further exploration.

Deciphering Antibiotic-Targeted Metabolic Pathways in Acinetobacter baumannii: Insights from Transcriptomics and Genome-Scale Metabolic Modeling

In the ongoing battle against antibiotic-resistant infections, Acinetobacter baumannii has emerged as a critical pathogen in healthcare settings. To understand its response to antibiotic-induced stress, we integrated transcriptomic data from various antibiotics (amikacin sulfate, ciprofloxacin, polymyxin-B, and meropenem) with metabolic modeling techniques. Key metabolic pathways, including arginine and proline metabolism, glycine–serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, and propanoate metabolism, were significantly impacted by all four antibiotics across multiple strains. Specifically, biotin metabolism was consistently down-regulated under polymyxin-B treatment, while fatty acid metabolism was perturbed under amikacin sulfate. Ciprofloxacin induced up-regulation in glycerophospholipid metabolism. Validation with an independent dataset focusing on colistin treatment confirmed alterations in fatty acid degradation, elongation, and arginine metabolism. By harmonizing genetic data with metabolic modeling and a metabolite-centric approach, our findings offer insights into the intricate adaptations of A. baumannii under antibiotic pressure, suggesting more effective strategies to combat antibiotic-resistant infections.

Impact of the Supply Chain on Sustainable and Business Performance During the COVID-19 Pandemic

The main objective of this empirical study is to provide empirical evidence that allows quantifying the impact of the supply chain on sustainable and firm performance during the COVID-19 pandemic. An electronic survey was applied to collect information on 65 manufacturing firms selected through simple random sampling, using the statistical technique of Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data obtained using the SmartPLS 4.0 software. Furthermore, the analysis carried out in this study identifies a need for more theoretical and, above all, empirical evidence in this area, which prevents the generalization of the results obtained. Likewise, the results suggest that during the COVID-19 pandemic, the supply chain positively impacted sustainable performance. However, an adverse effect was also detected on firm performance, so it is possible to conclude that the COVID-10 pandemic substantially improved the sustainable performance of manufacturing companies. However, it also generated a decrease in their level of firm performance.

Modeling porosity and permeability evolution of burrow fillings with packstone fabric in the Upper Jurassic Hanifa Formation, central Saudi Arabia: A digenetic backstripping study

Understanding the development and characteristics of burrow fillings (BF) is crucial for predicting porosity, permeability, and fluid flow behavior in bioturbated carbonate reservoirs. Advanced imaging, digital rock modeling, and backstripping techniques offer valuable insights into this development. In this study, we examined the BF in the bioturbated strata of the Upper Jurassic Hanifa Formation (central Saudi Arabia) to understand how sedimentological and diagenetic processes—such as compaction, dolomitization, and dissolution—influence porosity and permeability over time.Backstripping results provide significant insights into the evolution of porosity and permeability within packstone BF of the studied strata. We found that compacted, grain-supported BF exhibit limited improvement in porosity and permeability post-compaction. In contrast, less compacted intervals with open fabric BF can achieve up to 20% porosity and > 5000 mD permeability in their grainstone versions. Notably, even with over 10% mud content, open fabric BF maintained substantial permeability (>1000 mD) due to a connected pore network. Moreover, these BF have the potential to recover porosity and permeability after complete pore network occlusion by the mud matrix through diagenetic processes like preferential dolomitization of the mud matrix and subsequent dissolution.The findings underscore the dynamic evolution of porosity in packstones within BF of bioturbated strata. Initially, mud-rich packstones with low porosity and permeability can transform into more permeable packstones through dolomitization and dissolution. Conversely, initially high-quality, mud-poor packstones with high porosity and permeability may degrade due to compaction. The quantification of porosity and permeability using digital rock modeling and backstripping techniques provides an objective and comprehensive understanding of these evolving properties, highlighting key phases and processes affecting reservoir quality. The approach of integrating digital rock modeling and backstripping technique enhances the ability to predict subsurface storage and flow capacity in bioturbated strata.