Failure Time Research Articles

A real world analysis of secondary BRAF variations after targeted therapy resistance in driver gene positive NSCLC

Secondary BRAF variations have been identified as a mechanism of resistance to tyrosine kinase inhibitors (TKIs) in patients with driver gene-positive NSCLC. Nevertheless, there is still a lack of consensus regarding the characteristics and subsequent treatment strategies for these patients. We retrospectively reviewed the medical records of patients with driver gene-positive NSCLC who received TKIs therapy at Zhejiang Cancer Hospital between May 2016 and December 2023. The clinical and genetic characteristics of these patients were assessed, along with the impact of various treatment strategies on survival. This study enrolled 27 patients with advanced NSCLC, in whom BRAF variations occurred at a median time of 28 months after the initiation of targeted therapy. The multivariate accelerated failure time (AFT) model revealed that, compared to chemotherapy-based regimens group, the combined targeted therapy group (p < 0.001) and the combined local treatment group for oligo-progression (p < 0.001) significantly extended patient survival. In contrast, continuing the original signaling pathway's targeted monotherapy was associated with shorter survival (p = 0.034). The median global OS for each treatment group was as follows: chemotherapy-based regimens group, 45 months; combined targeted therapy group, 59 months; combined local treatment group for patients with oligo-progression, 46 months; and targeted monotherapy group, 36 months. Study results indicate that the combination targeted therapy group (including TKIs, BRAF inhibitors, and/or MEK inhibitors) and the localized treatment group are more effective than traditional chemotherapy-based regimens in improving survival. Additionally, continuing targeted monotherapy along the original signaling pathway proves less effective than chemotherapy-based regimens.

Exploring time-to-event analysis models: A study of accelerated failure time and weighted least square estimation

Background: This study explores time-to-event analysis models, focusing on the comparison between accelerated failure time (AFT) and weighted least square estimation (WLSE) methods in analyzing survival data, emphasizing the challenge of heteroscedasticity in real-world datasets. Methods: Using the North Central Cancer Treatment Group (NCCTG) Lung Cancer dataset, five parametric AFT distributions (exponential, Weibull, log-logistic, log-normal, and generalized gamma) were evaluated. The log-likelihood method was utilized to calculate the AIC and BIC values for each distribution to determine the best fit model. Results: Among the different distributions tested, the Weibull distribution was identified as the best fit model based with the lowest AIC and BIC values of 2311.70 and 2318.56, respectively. This distribution played a crucial role in determining factors associated with the cancer data. Specifically, the variablecalories consumed at a meal (meal.cal) was associated with cancer patient recovery, with an estimate of 0.00004 and a standard error of 0.0002. Results indicated homoscedastic data through the Breusch-Pagan test. Moreover, the results demonstrated that when dealing with homoscedastic data, the Weibull AFT model outperformed WLSE in producing statistically significant effects and precise estimates. Conclusion: For data exhibiting homoscedasticity, the AFT method yielded superior results compared to WLSE in terms of statistical significance and precision. Therefore, it is advisable to employ AFT for homoscedastic data and WLSE for heteroscedastic data to enhance the accuracy of covariate effect estimates in survival analysis.

Fault analysis of oil-immersed transformer based on digital twin technology

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.

Vaginal and endometrial microbiome dysbiosis associated with adverse embryo transfer outcomes

BackgroundAssisted reproductive technology (ART) is the most effective method to treat infertility and the pathogenesis of implantation failure after in vitro fertilization-embryo transfer (IVF-ET) is a challenging filed in infertility. Microbes in the female reproductive tract are considered to be associated with gynecological and obstetric diseases. However, its effects on embryo implantation failure are unsured.PurposeThis study aimed to investigate reproductive tract dysbiosis, identify different bacteria in reproductive tract as potential biomarkers of embryo implantation failure and demonstrate the pathogenesis through metabolites analysis.MethodsWe compared the data from 16S rRNA gene and metagenome in reproductive tracts through QIIME2 and HUMAnN2 by the times of embryo implantation failure on 239 infertile patients and 17 healthy women.ResultsOur study revealed a strong positive correlation between Lactobacillus abundance and embryo implantation success (IS) after IVF-ET. The microbial community composition and structure in reproductive tract showed substantially difference between the embryo implantation failure (IF) and healthy control. Moreover, we established a diagnostic model through receiver operating characteristic (ROC) with 0.913 area under curve (AUC) in IS and multiple implantation failures (MIF), verified its effectiveness with an AUC = 0.784 demonstrating microbial community alterations could efficiently discriminate MIF patients. Metagenome functional analyses of vaginal samples from another independent infertile patients after IVF-ET revealed the L-lysine synthesis pathway enriched in IF patients, along with ascended vaginal pH and decreased Lactobacillus abundance.ConclusionsThis study clarifies several independent relationships of bacteria in vagina and endometrial fluid on embryo implantation failure and undoubtedly broadens the understanding about female reproductive health.

RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and Autoencoder Modeling

As an important part of industrial equipment, the safe and stable operation of rolling bearings is an important guarantee for the performance of mechanical equipment. Aiming at the problem that it is difficult to characterize the running state of rolling bearings, this paper mainly analyzes and processes the vibration signals of rolling bearings, extracts and fuses multi-information entropy, and monitors the running state of rolling bearings and predicts the remaining useful life prediction (RUL) through test verification. Firstly, in view of the difficulty in characterizing the bearings running state characteristics, a rolling bearings running state monitoring method based on multi-information entropy fusion and denoising autoencoder (DAE) was proposed to extract the multi-entropy index features of vibration signals to improve the accuracy of feature extraction, and to solve the problem of not obvious information representation of a single feature indicator and missing information in the feature screening process. Secondly, in view of the problems of low prediction accuracy and poor robustness and generalization in traditional RUL models, a rolling bearings RUL model combining convolutional autoencoder (CAE) and bidirectional long short-term memory network (BiLSTM) was proposed. The introduction of convolution operation made CAE have the feature of weight sharing, reducing the complexity of the model. Finally, the XJTU-SY data set was used to verify the constructed model. The results show that the condition monitoring model established in this paper can accurately evaluate the running state of the rolling bearing and accurately locate the failure time. At the same time, the residual life prediction model can realize the residual life prediction of most data sets, and has good accuracy and robustness.

Factors affecting the tensile strength of bituminous geomembrane seams

The tensile shear strength, break location, and constant tensile load failure times are examined for seams made from one 4 mm-thick bituminous geomembrane (BGM) product, with corresponding observations specific to that material. In short-term tests, failure is observed within the sheet material once the seam strength exceeded 0.8 times that of the sheet material. The effects of seam thickness reduction and overlap width on seam strength are examined for two methods of seaming. Seams with a short-term strength meeting or exceeding 80% of the sheet strength are subjected to constant tensile loads between 18 and 55% of sheet ultimate strength and the time to failure is reported. The relationship between short-term seam strength and time to failure under sustained load and thickness reduction and squeeze-out is investigated. Constant tensile load testing is proposed as a construction quality assurance procedure to assess the degree of geotextile engagement of field seams.

Development and validation of accelerated failure time model for cause-specific survival and prognostication of oral squamous cell carcinoma: SEER data analysis.

Oral Squamous Cell Carcinoma is the most prevalent malignancies affecting the oral cavity. Despite progress in studies and treatment options its outlook remains grim with survival prospects greatly affected by demographic and clinical factors. Precisely predicting survival rates and prognosis plays a role in making treatment choices for the best achievable overall health outcomes. To develop and validate an accelerated failure time model as a predictive model for cause-specific survival and prognosis of Oral Squamous Cell Carcinoma patients and compare its results to the traditional Cox proportional hazard model. We screened Oral cancer patients diagnosed with Squamous Cell Carcinoma from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2020. An accelerated failure time model using the Type I generalized half logistic distribution was used to determine independent prognostic factors affecting the survival time of patients with oral squamous carcinoma. In addition, accelerated factors were estimated to assess how some variables influence the survival times of the patients. We used the Akaike Information Criterion, Bayesian Information Criterion to evaluate the model fit, the area under the curve for discriminability, Concordance Index (C-index) and Root Mean Square Error and calibration curve for predictability, to compare the type I generalized half logistic survival model to other common classical survival models. All tests are conducted at a 0.05 level of significance. The accelerated failure time models demonstrated superior effectiveness in modeling (fit and predictive accuracy) the cause-specific survival (CSS) of oral squamous cell carcinoma compared to the Cox model. Among the accelerated failure time models considered, the Type I generalized half logistic distribution exhibited the most robust model fit, as evidenced by the lowest Akaike Information Criterion (AIC = 27370) and Bayesian Information Criterion (BIC = 27415) values. This outperformed other parametric models and the Cox Model (AIC = 47019, BIC = 47177). The TIGHLD displayed an AUC of 0.642 for discrimination, surpassing the Cox model (AUC = 0.544). In terms of predictive accuracy, the model achieved the highest concordance index (C-index = 0.780) and the lowest root mean square error (RMSE = 1.209), a notable performance over the Cox model (C-index = 0.336, RMSE = 6.482). All variables under consideration in this study demonstrated significance at the 0.05 level for CSS, except for race and the time span from diagnosis to treatment, in the TIGHLD AFT model. However, differences emerged regarding the significant variations in survival times among subgroups. Finally, the results derived from the model revealed that all significant variables except chemotherapy, all TNM stages and patients with Grade II and III tumor presentations contributed to the deceleration of time to cause-specific deaths. The accelerated failure time model provides a relatively accurate method to predict the prognosis of oral squamous cell carcinoma patients and is recommended over the Cox PH model for its superior predictive capabilities. This study also underscores the importance of using advanced statistical models to improve survival predictions and outcomes for cancer patients.

Protective coating performance for structural integrity of polymer composites in fire: Novel bench scale instrument design and coupon level test

The loss of structural integrity of fiber-reinforced polymer composites (FRPs) when set on fire poses a great threat for the safety of occupants and property, which can be alleviated by introducing fire protective methods. The fire-protective polymeric coating has shown great potential in reducing fire hazards of FRPs and preventing loss of post-fire mechanical properties, but it has seldom been proven to have a protective effect under simultaneous loading and fire damage on a coupon level using a lab scale instrument. In this work, a machine which can be mounted on the widely acknowledged cone calorimeter (CCT) was designed and fabricated with details provided. Two types of laminate were prepared to be subjected to a static axial or off-axial load while set ignited and burned. Results showed that the protective polymeric coating delayed coated coupons for mechanical failure time by more than 190 % compared to coupons without any coating. With this instrument being universally adaptable to various types of CCT, it provided meaningful information to the materials design process of fire-safe FRPs.

Vibration test and fatigue test for failure probability evaluation method with integrated energy

The seismic evaluation of key components such as reactor vessel is important for the Seismic Probabilistic Risk Assessment (S-PRA) in a Sodium-Cooled Fast Reactor (SFR). Many components were fractured by integrated damage like fatigue damage during seismic ground motion. In this paper, failure probability evaluation method with integrated energy was developed by comparing the energy with vibration tests and fatigue tests. Vibration tests were performed to evaluate integrated vibration energy at failure by energy balance equation, and fatigue tests were performed to evaluate integrated vibration energy at failure based on experimental results. As results, it is shown that integrated energy at failure time by vibration tests were estimated and its values were in range the energy based on results of fatigue tests.

New correlations for focusing effect evaluation of the light metal layer in the lower head of a nuclear reactor in case of severe accident

In case of a severe accident (SA) in a nuclear reactor, the core heats up and materials melt and relocate to the lower head of the vessel. In such configuration, stratification occurs between the oxide and metal phases present in the melt. When the metal phase is lighter than the oxide phase, the maximum heat flux applied to the vessel wall is concentrated at the location of this top metal layer, creating the so-called “focusing effect”. The modelling of the focusing effect is essential for SA codes to properly evaluate the vessel failure time or to demonstrate the vessel integrity, when In Vessel Retention (IVR) strategy is implemented. However, existing correlations used in SA codes for focusing effect modelling have some limitations: if the thickness of the metal layer becomes very small, they predict an extremely high heat flux. Moreover, they were validated with tests using water, whereas the Prandtl number was found to play a significant role in this heat transfer.In this paper, the focusing effect is studied based on 3D Direct Numerical Simulations (DNS) of a cylindrical metal layer, covering all possible metal layer characteristics in terms of geometry and boundary conditions, especially for its top surface where the intensity of radiative heat transfer may vary. The analysis of the fluid behavior and the quantitative results obtained allow to derive new correlations and a new model based on the radial profile of the mean temperature of the system and on the radial profile at the top surface. Results at reactor scale confirm the overestimation of existing 0D model used in SA codes for a thin metal layer and highlight the relevance of the proposed model for focusing effect evaluation during transient situations.

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.

Interval-Censored Linear Quantile Regression

Censored quantile regression has emerged as a prominent alternative to classical Cox’s proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While quantile regression has been extensively studied for right-censored survival data, methodologies for analyzing interval-censored data remain limited in the survival analysis literature. This article introduces a novel local weighting approach for estimating linear censored quantile regression, specifically tailored to handle diverse forms of interval-censored survival data. The estimation equation and the corresponding convex objective function for the regression parameter can be constructed as a weighted average of quantile loss contributions at two interval endpoints. The weighting components are nonparametrically estimated using local kernel smoothing or ensemble machine learning techniques. To estimate the nonparametric distribution mass for interval-censored data, a modified EM algorithm for nonparametric maximum likelihood estimation is employed by introducing subject-specific latent Poisson variables. The proposed method’s empirical performance is demonstrated through extensive simulation studies and real data analyses of two HIV/AIDS datasets. Supplementary materials for this article are available online.

Incidence of prostate, colorectal and male breast cancers in relation with statins and testosterone replacement therapy: SEER-Medicare 2007–2015

IntroductionStatins and testosterone replacement therapy (TTh) have been inconsistently associated with a reduced risk of hormone-related cancers (HRCs, prostate [PCa], colorectal [CRC], and male breast cancers [BrCa]). Yet, the joint association of statins and TTh with the incidence of these cancers, and whether these associations vary by race, remains poorly understood. The objective of this retrospective cohort study is to examine the independent and joint effects of pre-diagnostic use of statins and TTh on the risk of HRCs, including PCa, CRC, and male BrCa. Materialsand Methods: In 105,690 men (≥65 yrs) identified using the SEER-Medicare 2007–2015 data, we identified 82,578 White and 10,256 Black men. Pre-diagnostic prescription of statins and TTh was ascertained for this analysis and categorized into four groups (Neither users, statins alone, TTh alone and Dual users). Multivariable Time-varying Cox proportional hazards and Accelerated Failure Time (AFT) models were performed. ResultsWe found inverse joint associations of statins and TTh with incident HRCs before (aHR: 0.39; 95 % CI: 0.35–0.44) and after 3 years of follow-up (aHR: 0.74; 95 % CI: 0.67–0.82). This included a lower risk for advanced stage HRC (only <3 years follow-up). Similar joint associations were identified with incident PCa, aggressive PCa, incident CRC, and its specific right- and left-sided CRC (only <3 years follow-up). In general, the inverse associations persisted among White (mainly <3 years follow-up) and Black men (high-grade HRC and <3 years follow-up). Findings from the AFT analysis were similar. DiscussionPre-diagnostic use of statins and TTh were, independently and jointly, associated with reduced risks of HRC and specific cancer sites at three years of follow-up overall, and among White and Black men. Greatest associations of HRCs risk reduction were observed among dual users (statins plus TTh). Further studies are needed to validate these findings, including larger samples of Black men, and male BrCa sites.

Conditional quasi‐likelihood inference for mean residual life regression with clustered failure time data

AbstractIn the analysis of clustered failure time data, Cox frailty models have been extensively studied by incorporating frailty with a prespecified distribution to address potential correlation of data within clusters. In this paper, we propose a frailty proportional mean residual life regression model to analyze such data. A novel conditional quasi‐likelihood inference procedure is developed, utilizing a stochastic process and the inverse probability of censoring weighting (IPCW) to form estimating equations for regression parameters. Our proposal employs conditional inference based on a penalized quasi‐likelihood to address within‐cluster correlation without need to specify the frailty distribution, bringing the method closer to what suffices for real‐world applications. By adopting the Buckley–James estimator in the IPCW, the method further allows for dependent censoring. We establish asymptotic properties of the proposed estimator and evaluate its finite sample performance via simulation studies. An application to the data from a multi‐institutional breast cancer study is presented for illustration.

Densely stocked neighborhood reduces the risk of tree mortality in drylands: Evidence from Populus euphratica forests along the Tarim River corridor

To mitigate forest loss under current environmental challenges, it is increasingly urgent to model how tree mortality is mediated by both biotic and abiotic agents. However, the role of neighborhood interactions in mediating tree survival in dryland forests has been poorly understood, as it is often presupposed to be irrelevant in low-density stands coupled with overriding drought stress. Here, we conducted a failure time analysis of Populus euphratica forests (known as Tugay forests) in the Tarim River corridor (NW China) to examine the presupposition. We collected a spatial-explicit population inventory dataset with >7000 trees on ca. 14-ha land area with varying vapor pressure deficit (VPD), groundwater table depth (GWD), and stand density. Hegyi's competition index (CI) and stand density index (SDI, the number of large trees with an equivalent diameter of 25.4 cm) were employed to characterize neighborhood structures of each individual tree. Aridity stresses (higher VPD and GWD) tended to elevate tree mortality risk, and showed weaker effects than neighborhood structures did. Tree mortality risk increased drastically with higher CI, suggesting a strong role of competition for critical water resource. However, tree mortality risk reduced with higher SDI, likely due to ameliorated stresses of excessive light and/or temperature under less open canopies. This study highlights the importance of localized inter-tree interactions in regulating tree populations under prevalent drought stress and reiterates classic cautions against inferences about competition/facilitation in absence of spatial-explicit contexts. We recommend the priority of preserving large trees aggregated as closed stands in dryland forest conservation.

Mandibular dose-volume predicts time-to-osteoradionecrosis in an actuarial normal-tissue complication probability (NTCP) model: External validation of right-censored clinico-dosimetric and competing risk application across international multi-institutional observational cohorts and online graphical user interface clinical support tool assessment.

Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors. Demographic, clinical and dosimetric data were retrospectively obtained for a clinical observational cohort of 1129 patients with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. ORNJ was diagnosed in 198 patients (18%). A multivariable logistic regression analysis with forward stepwise variable selection identified significant predictors for ORNJ. These predictors were then used to train a Weibull Accelerated Failure Time (AFT) model, which was externally validated using an independent cohort of 265 patients (92 ORNJ cases and 173 controls) treated at Guy's and St. Thomas' Hospitals. Our model identified that each unit increase in D25% is significantly associated with a 12% shorter time to ORNJ (Adjusted Time Ratio [ATR] 0·88, p<0·005); pre-RT dental extractions was associated to a 27% faster (ATR 0·73, p=0·13) onset of ORNJ; male patients experienced a 38% shorter time to ORNJ (ATR 0·62, p = 0·11). The model demonstrated strong internal calibration (integrated Brier score of 0·133, D-calibration p-value 0.998) and optimal discrimination at 72 months (Harrell's C-index of 0·72). The model also showed good generalization to the independent cohort, despite a slight drop in performance. This study is the first to demonstrate a direct relationship between radiation dose and the time to ORNJ onset, providing a novel characterization of the impact of delivered dose not only on the probability of a late effect (ORNJ), but the conditional risk during survivorship. This work was supported by various funding sources including NIH, NIDCR, NCI, NAPT, NASA, BCM, Affirmed Pharma, CRUK, KWF Dutch Cancer Society, NWO ZonMw, and the Apache Corporation.

Optimal subsampling for semi-parametric accelerated failure time models with massive survival data using a rank-based approach.

Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure time (AFT) models, its application to semi-parametric AFT models with rank-based estimation have received limited attention. The challenges arise from the non-smooth estimating function for regression coefficients and the seemingly zero contribution from censored observations in estimating functions in the commonly seen form. To address these challenges, we develop optimal subsampling probabilities for both event and censored observations by expressing the estimating functions through a well-defined stochastic process. Meanwhile, we apply an induced smoothing procedure to the non-smooth estimating functions. As the optimal subsampling probabilities depend on the unknown regression coefficients, we employ a two-step procedure to obtain a feasible estimation method. An additional benefit of the method is its ability to resolve the issue of underestimation of the variance when the subsample size approaches the full sample size. We validate the performance of our estimators through a simulation study and apply the methods to analyze the survival time of lymphoma patients in the surveillance, epidemiology, and end results program.

Joint modelling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment in the U.S.

Abstract The motivation for this paper is to determine factors associated with time-to-fertility treatment (TTFT) among women currently attempting pregnancy in a cross-sectional sample. Challenges arise due to dependence between time-to-pregnancy (TTP) and TTFT. We propose appending a marginal accelerated failure time model to identify risk factors of TTFT with a model for TTP where fertility treatment is included as a time-varying treatment to account for their dependence. The latter requires extending backwards recurrence survival methods to incorporate time-varying covariates with time-varying coefficients. Since backwards recurrence survival methods are a function of mean survival, computational difficulties arise in formulating mean survival when fertility treatment is unobserved, i.e. when TTFT is censored. We address these challenges by developing computationally friendly forms for the double expectation of TTP and TTFT. The performance is validated via comprehensive simulation studies. We apply our approach to the National Survey of Family Growth and explore factors related to prolonged TTFT in the U.S.

Effect of SO2 and NH3 on the corrosion failure of Zn-Al-Mg coatings

This study investigates the corrosion failure mechanism of Zn-Al-Mg (ZAM) coatings in environments containing sulfur dioxide (SO2) and ammonia (NH3) gas through accelerated corrosion tests. A comparison with Galvalume (GL) coatings reveals that ZAM coatings exhibit lower corrosion rates than GL coatings in SO2-containing environments, but display higher corrosion rates in NH3-containing environments. Analysis of the cut-edge corrosion of ZAM-coated steel in these environments reveals noticeable delamination phenomenon, with significant discrepancies observed in the corrosion failure times among different cut-edges of the same sample. This disparity can be attributed to both the uneven distribution of coating caused by processing techniques and the detrimental effects of SO2 and NH3 on the corrosion resistance of surface corrosion products. Notably, the diminished corrosion resistance in NH3-containing environments underscores the pivotal role of environmental factors in coating failure.

A multi‐phase mechanical model of biochar–cement composites at the mesoscale

AbstractThis study presents a five‐phase mesoscale modeling framework specifically developed to investigate crack propagation and mechanical properties of biochar–cement composites. The multi‐phase model includes porous biochar particles with precise geometric construction, sand aggregates, cement matrix, and interfacial transition zone adjunct to both the biochar particles and sand aggregates. The 3D porous biochar library was first proposed and established in this study, which could provide an external interface for describing different pore shapes, wall thicknesses, and pore areas. All the simulation results were experimentally validated using a digital image correlation. Through precise geometric modeling, the unique failure modes and timing of biochar particles within the mortar were identified. This is analogous to the “strong column–weak beam” concept, accounting for the enhanced ductility observed in the biochar–cement composites under compression test. This work can advance the geometric modeling of porous aggregates broadly and elucidate their mesoscopic failure mechanisms in cementitious materials, thus providing new insights for developing high‐ductility and lightweight cement composites.