Probability-based Method Research Articles

Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

Novel Frequency Regulation Scenarios Generation Method Serving for Battery Energy Storage System Participating in PJM Market

As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.

Thermomechanical responses of steel frames under aftershock after fires

A novel advanced fiber beam-column element, which incorporates stability functions and the fiber distributed plasticity model to capture geometric and material nonlinearities, respectively, has been successfully developed to predict the nonlinear inelastic thermo-mechanical behaviors of steel frames subjected to aftershock after fires. To address the nonlinear dynamic problems resulting from aftershocks after fires, a sequential nonlinear thermal incremental-iterative solution scheme has been developed, which is based on the Newton-Raphson algorithm and Newmark-β method. In addition, a transcendental probability-based method is employed to express the statistical characteristics of aftershock intensity relative to the mainshock. The proposed method is applied to analyze three framed structures: a steel portal frame, a two-story plane steel frame, and a four-story asymmetric space steel frame. This analysis shows the accuracy, computational performance, and applicability of the proposed method. Remarkably, the results obtained from the code work, using just one or two elements per member, are in close agreement with all the results analyzed by Abaqus. This emphasizes that the proposed method effectively predicts the response of framed structures under aftershock after fires with excellent computational efficiency, significantly surpassing traditional finite element approaches in commercial software like Abaqus. The successful results of the dynamic analysis of the frames under aftershocks following fires underscore the importance of considering aftershocks after a fire incident. It is demonstrated that the occurrence of an aftershock accelerates the failure of the frame compared to the case when the frame is subjected to fire alone. This highlights the critical need to account for aftershocks when assessing the safety and structural integrity of framed structures after a fire event.

A roundtrip probability estimation method for mechanical equipment fault detection under imbalanced samples

Aiming at high misdetection of mechanical faults under imbalanced samples, a roundtrip probability-based method is proposed. By roundtrip mapping between latent variables and real fault data, biased estimation of the probability distribution of real fault data is obtained. Further, virtual fault data are sampled according to such distribution to increase sample amount. For recognition of real and virtual data, loss function based on binary cross-entropy is designed. For reconstruction between fault data and its roundtrip mapped results, objective function based on mean square error is designed. Thus, it preserves boundary data and avoids too many virtual data in central area. Meanwhile, a strategy for eliminating abnormal samples is designed to reduce boundary deviation. For supporting the advantage of roundtrip, in-depth reasons for misdetection are analyzed from empirical risk and structural risk. Experiments on 30 benchmark imbalanced test sets show that fault detection rate increases after amount enhancement. Additionally, it is verified on blade cracking and bearing fault detection. Results show that F1 score increases from 0.485 to 0.51 and 0.725 to 0.775 for such two cases.

Phishing Message Detection Based on Keyword Matching

This paper proposes to use the Naïve Bayes-based algorithm for phishing detection, specifically in spam emails. The paper compares probability-based and frequency-based approaches and investigates the impact of imbalanced datasets and the use of stemming as a natural language processing (NLP) technique. Results show that both algorithms perform similarly in spam detection, with the choice between them depending on factors such as efficiency and scalability. Accuracy is influenced by the dataset configuration and stemming. Imbalanced datasets lead to higher accuracy in detecting emails in the majority class, while they struggle to classify minority-class emails. In contrast, balanced datasets yield overall high accuracy for both spam and ham email identification. This study reveals that stemming has a minor impact on algorithm performance, occasionally decreasing in accuracy due to word grouping. Balancing the dataset is crucial for improving algorithm performance and achieving accurate spam email detection. Hence, both probability-based and frequency-based Naïve Bayes algorithms are effective for phishing detection using balanced datasets. The frequency-based approach, with a balanced dataset and stemming, achieves a balanced performance between recall and precision, while the probability-based method with a balanced dataset and no stemming prioritises overall accuracy.

An expeditious framework for assessing the seismic resilience (SR) of structural configurations

The assessment of the Seismic Resilience (SR) has become a fundamental issue in Structural Engineering to ensure rapid and efficient recovery to earthquake scenarios. Even if there are methodologies that assess SR, most of them are significantly time-consuming. In this paper, a framework for simplified assessments of SR is proposed based on the existing literature. The probability-based method of fragility curves is applied to estimate the losses and a novel definition of the recovery model is herein proposed. A case study is discussed to apply the proposed framework to two configurations: a fixed-base and a base-isolated structural system. The state-of-the-art platform Opensees has been applied to calculate the non-linear performance of the structures. The longitudinal drift ratio was chosen as the representative parameter to develop fragility curves and thus to quantify SR. The response of the 200 analyses (100 for each system) shows the benefit of applying base isolation technique in terms of SR, used as a comprehensive parameter to compare the two solutions. The proposed approach may be helpful for many stakeholders and decision makers for pre- and post-earthquake assessments.

Approach of Solving Multi-objective Programming Problem by Means of Probability Theory and Uniform Experimental Design

In this paper, an approach to deal with the multi-objective programming problem is regulated by means of probability-based multi-objective optimization, discrete uniform experimental design, and sequential algorithm for optimization. The probability-based method for multi-objective optimization is used to conduct conversion of the multi-objective optimization problem into a single-objective optimization one in the viewpoint of probability theory. The discrete uniform experimental design is used to supply an efficient sampling to simplify the conversion. The sequential algorithm for optimization is employed to carry out further optimization. The corresponding treatments reveal the essence of the multiobjective programming, and consideration of the simultaneous optimization of each objective of multi-objective programming problem rationally. Two examples are conducted to illuminate the rationality of the approach.

Intrafraction tumor motion monitoring and dose reconstruction for liver pencil beam scanning proton therapy

Pencil beam scanning (PBS) proton therapy can provide highly conformal target dose distributions and healthy tissue sparing. However, proton therapy of hepatocellular carcinoma (HCC) is prone to dosimetrical uncertainties induced by respiratory motion. This study aims to develop intra-treatment tumor motion monitoring during respiratory gated proton therapy and combine it with motion-including dose reconstruction to estimate the delivered tumor doses for individual HCC treatment fractions. Three HCC-patients were planned to receive 58 GyRBE (n=2) or 67.5 GyRBE (n=1) of exhale respiratory gated PBS proton therapy in 15 fractions. The treatment planning was based on the exhale phase of a 4-dimensional CT scan. Daily setup was based on cone-beam CT (CBCT) imaging of three implanted fiducial markers. An external marker block (RPM) on the patient's abdomen was used for exhale gating in free breathing. This study was based on 5 fractions (patient 1), 1 fraction (patient 2) and 6 fractions (patient 3) where a post-treatment control CBCT was available. After treatment, segmented 2D marker positions in the post-treatment CBCT projections provided the estimated 3D motion trajectory during the CBCT by a probability-based method. An external-internal correlation model (ECM) that estimated the tumor motion from the RPM motion was built from the synchronized RPM signal and marker motion in the CBCT. The ECM was then used to estimate intra-treatment tumor motion. Finally, the motion-including CTV dose was estimated using a dose reconstruction method that emulates tumor motion in beam's eye view as lateral spot shifts and in-depth motion as changes in the proton beam energy. The CTV homogeneity index (HI) The CTV homogeneity index (HI) was calculated as . The tumor position during spot delivery had a root-mean-square error of 1.3mm in left-right, 2.8mm in cranio-caudal and 1.7mm in anterior-posterior directions compared to the planned position. On average, the CTV HI was larger than planned by 3.7%-points (range: 1.0-6.6%-points) for individual fractions and by 0.7%-points (range: 0.3-1.1%-points) for the average dose of 5 or 6 fractions. A method to estimate internal tumor motion and reconstruct the motion-including fraction dose for PBS proton therapy of HCC was developed and demonstrated successfully clinically.

Logic Circuits Reliability Analysis using Signal Probability and Bayesian Network Concepts

Background: Reliability analysis of logic circuits has been widely investigated due to increasing fault occurrence in modern integrated circuits. Simulation-based and analytical methods are developed to estimate the reliability of logic circuits. Methods: In this paper, a signal probability-based method is presented to estimate the reliability of logic circuits. In the proposed approach, four signal probabilities (correct 0, correct 1, incorrect 0, and incorrect 1) are derived (for every node of the circuit) using a probabilistic transfer matrix (PTM), and the correlation coefficients (CCs) are deployed to resolve the reconvergent fanouts issues. The CCs are defined in a fanout cone and are propagated through the logic gates to the related reconvergent nodes. The Bayesian network concept is applied to achieve more accuracy in the propagation of CCs through the logic gates. Results: The accuracy and scalability of the proposed method are proved by various simulations on benchmark circuits (ISCAS 85, LGSynth91, and ITC99). The proposed method efficiently solves the reconvergent fanout problem. Moreover, the proposed method outperforms the previous methods regarding accuracy and scalability. Conclusion: Simulation results on ISCAS 85, LGSynth91, and ITC99 benchmark circuits show less than 3% average error compared with the accurate simulation-based fault injection method.

Optimization of Steam Distillation Process for Volatile Oils from Forsythia suspensa and Lonicera japonica according to the Concept of Quality by Design

In this study, the process of steam distillation to collect volatile oils from Forsythia suspensa (F. suspensa) and Lonicera japonica (L. japonica) was optimized according to the concept of quality by design. First, the liquid/material ratio, distillation time, and collection temperature were identified as critical process parameters by a review of the literature and single-factor experiments. Then, a Box–Behnken design was used to study the quantitative relationship between the three process parameters, two raw material properties, and the yield of volatile oil. A mathematical model was established with an R2 value exceeding 0.90. Furthermore, the design space of the volatile oil yield was calculated by a probability-based method. The results of a verification experiment showed that the model was accurate and the design space was reliable. A total of 16 chemical constituents were identified in the volatile oil from mixtures of F. suspensa and L. japonica. The content of β-pinene was the highest (54.75%), and the composition was similar to that of the volatile oil of F. suspensa. The results showed that when F. suspensa and L. japonica were distilled together, the main contribution to the volatile oil was from F. suspensa. The volatile oil yield from the combination of F. suspensa and L. japonica was not higher than that from L. japonica.

Višekriterijumska optimizacija zasnovana na verovatnoći kao osnova za primenu novog robustnog dizajna na parametre mašinske obrade

Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.

Secured location-aware mobility-enabled RPL

Although there have been many studies on the Internet of Things (IoT), there are still major challenges for IoT to become ubiquitous. So far, the mobility management challenge has not been addressed well. Routing Protocol for Low-Power and Lossy Networks (RPL), which is known as the de-facto for routing in IoT, does not support mobile nodes. Some studies have tried to address this challenge, but they either caused a very high Packet Loss Rate (PLR) or produced lots of control packets. Also, they have not considered the security aspects of their work which is crucial for real-world applications. In this study, a novel extension for the RPL called Secured Location-Aware Mobility-enabled RPL (SLM-RPL) is proposed to facilitate the mobility management of RPL while considering security precautions. From the mobility management point of view, according to extensive evaluations, SLM-RPL greatly reduces the hand-off delay and PLR compared to other mobility management schemes, even in big, dense, or highly dynamic networks. Therefore, SLM-RPL is shown to be the best option to be used in IoT applications, especially loss-sensitive ones. Also, SLM-RPL produces small numbers of control packets and has a low memory overhead. Moreover, from the security point of view, a probability-based method has been proposed and embedded in SLM-RPL, which is shown to be able to reduce the negative impacts of DODAG Information Solicitation (DIS) attacks by more than 99%. Also, a performable attack on SLM-RPL called False-Location-Injection (FLI) attack has been introduced, and a lightweight hybrid-structured Intrusion Detection System (IDS) has been provided to counter this attack as well as Sybil, Rank, Sinkhole, and impersonation attacks. The proposed IDS uses a voting-based approach which, when the ψ parameter is adjusted, can mitigate the impact of False-Reporting and collusion attacks. According to the evaluations, the proposed IDS can counter the mentioned attacks in the presence of Collusion attackers in different scenarios with Accuracy ≥0.99.

Embedded draw-down constraint reward function for deep reinforcement learning

Money management, also known as asset allocation, is constantly at the forefront of research in the trading and investing fields. Since Markowitz established the current portfolio theory in 1952, it has drawn many experts to this intriguing topic. The Kelly criteria are one of the brightest stars among these new techniques. It provides an elegant solution for players and investors to get the best bidding fraction and maximize their logarithm worth over time. However, it ignores the fact that each investor has a different risk tolerance, and the proportion was calculated using the Kelly criterion without taking into account the downside risk. This paper attempts to develop a risk prediction model using a probability-based method and adjust the reward function of deep reinforcement learning to account for the downside risk. To summarize, rather than a naive reward function that solely optimizes the return, the improved deep reinforcement learning may consider an investor’s risk tolerance. Finally, we solely analyze the scenario of a single asset and employ DXY, GBP/USD, and EUR/USD as the underlying training and validation data sets. The outcome demonstrates that the adjustment to the reward mechanism produces an interesting performance. When the required MDD (Maximum draw-down) is greater than 3%, the likelihood is on average greater than 70%.

A probability-based efficient assessment of seismic wave scattering in complex topography with geo-property uncertainty

There is great randomness in determining geotechnical parameters, resulting in an uncertain local site effect caused by seismic wave scattering such as valleys, hills, basins, etc. It is significant to develop the probability-based assessment method to quantify the local site effect considering the geo-property uncertainty. The multiplicative dimensional reduction method (M-DRM) is employed to approximate statistical moments of the response of the local topography with random parameters. A multi-dimensional random problem is converted into a series of deterministic analyses, solved by the boundary element method. The probability distribution estimation can be obtained based on the maximum entropy algorithm. This approach is used to solve the seismic wave scattering of fossil river courses under P-waves, and the applicability is verified compared to the Monte Carlo simulation. The results show that M-DRM can assess the seismic wave scattering by local topography with high efficiency. The seismic wave scattering inhomogeneously magnifies the geo-property uncertainty in wave propagation. The displacement variability is primarily related to the incident frequency, and the response under high-frequency P-waves has significant variability. It is the reason that the effect of the geo-property uncertainty on the peak ground acceleration is determined by the energy distribution of the incident wave.

Optimization of Steam Distillation Process and Chemical Constituents of Volatile Oil from Angelicaesinensis Radix

In this study, the steam distillation process of volatile oil from Angelicaesinensis Radix was optimized according to the concept of quality-by-design. A homemade glass volatile oil extractor was used to achieve better cooling of the volatile oil. First, the soaking time, distillation time, and liquid–material ratio were identified as potential critical process parameters by consulting the literature. Then, the three parameters were investigated by single factor experiments. The volatile oil yield increased with the extension in the distillation time, and first increased and then decreased with the increase in soaking time and liquid–material ratio. The results confirmed that soaking time, distillation time, and liquid–material ratio were all critical process parameters. The kinetics models of volatile oil distillation from Angelicaesinensis Radix were established. The diffusion model of spherical particle was found to be the best model and indicated that the major resistance of mass transfer was the diffusion of volatile oil from the inside to the surface of the medicinal herb. Furthermore, the Box–Behnken experimental design was used to study the relationship between the three parameters and volatile oil yield. A second-order polynomial model was established, with R2 exceeding 0.99. The design space of the volatile oil yield was calculated by a probability-based method. In the verification experiments, the average volatile oil yield reached 0.711%. The results showed that the model was accurate and the design space was reliable. In this study, 21 chemical constituents of volatile oil from Angelicaesinensis Radix were identified by gas chromatograph-mass spectrometer(GC-MS), accounting for 99.4% of the total volatile oil. It was found that the content of Z-ligustilide was the highest, accounting for 85.4%.

Dynamic multiobjective evolutionary algorithm with adaptive response mechanism selection strategy

In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed to address the shortcoming that a single response mechanism is suitable only for solving a certain type of dynamic multiobjective optimization problem. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D), and it is denoted as the MOEA/D-ARMS. Unlike the existing approaches, the ARMS strategy can adaptively select effective response mechanisms from the response mechanism pool based on the recent performance of each response mechanism. Four representative response mechanisms are selected to form the response mechanism pool. An overall evaluation strategy that assigns rewards to the response mechanism is adopted, and a probability-based method that is used to decide which response mechanism can be used to generate a new solution is employed. The proposed MOEA/D-ARMS algorithm is tested on two groups of test instances and compared with the decomposition-based and dominance-based DMOEAs. The results of the proposed MOEA/D-ARMS algorithm are superior to the compared algorithms, demonstrating its effectiveness.

Assessing social structure: a data-driven approach to define associations between individuals

Our interpretation of animal social structures is inherently dependent on our ability to define association criteria that are biologically meaningful. However, association thresholds are often based upon generalized preconceptions of a species’ social behaviour, and the impact of using these arbitrary definitions has been largely overlooked. In this study we suggest a probability-based method for defining association thresholds using lagged identification rates on photographic records of identifiable individuals. This technique uses a simple model of emigration/immigration from photographable clusters to identify the time-dependent lag value between identifications of two individuals that corresponds to approximately 75% probability of being in close spatial proximity and likely associating. This lag value is then used as the threshold to define associations for social analyses. We applied the technique to a dataset of northern resident killer whales (Orcinus orca) in the Northeast Pacific and tested its performance against two arbitrary thresholds. The probabilistic association maximized the variation in association strengths at different levels of the social structure, in line with known social patterns in this population. Furthermore, variability in inferred social structure metrics generated by different association criteria highlighted the consequential effect of choosing arbitrary thresholds. Data-driven association thresholds are a promising approach to study populations without the need to subjectively define associations in the field, especially in societies with prominent fission–fusion dynamics. This method is applicable to any dataset of sequential identifications where it can be assumed that associated individuals will tend to be identified in close proximity.

A probabilistic strategy to evaluate scour around bridge deepwater foundations considering a reliability assessment

Currently, an increasing number of deepwater foundations have been built in marine regions, and most of them have to deal with scour problems. Recent design codes of superstructures usually adopt the probability-based method, in which loads are treated as probabilistic parameters. However, for the design of substructures, the impact of scour is generally regarded as a deterministic value represented by the estimated scour depth. This inconsistency in the design strategies brings difficulties in considering the uncertainties associated with scour effects. Fundamentally, this is due to the challenges in the scour estimation using a probabilistic method. Based on an augmented scour database analyzed using the artificial neural-network (ANN), this study quantifies the uncertainties of four types of scour predictive methods. Based on this, it proposes a probabilistic approach for scour estimation according to different cases. Six typical conditions and two practical cases were analyzed using the probabilistic approach. The overall reliability index showed that all these existing scour estimation methods have certain limitations, including inconsistent reliability and unreliable scour depth predictions. To prevent these problems, a concept based on the probability of nonexceedance of a given scour depth has been proposed, which was determined by different target reliability requirements.

A comparison of three ways to assemble wall-to-wall maps from distribution models of vegetation types

ABSTRACT Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as nature management and environmental planning. The paper compares the performance of three methods for assembling predictions for multiple vegetation types, modeled individually, into a wall-to-wall map. The authors used grid-cell based probability surfaces from distribution models of 31 vegetation types to test the three assembly methods. The first, a probability-based method, selected for each grid cell the vegetation type with the highest predicted probability of occurrence in that cell. The second, a performance-based method, assigned the vegetation types, ordered from high to low model performance, to a fraction of the grid cells given by the vegetation type’s prevalence in the study area. The third, a prevalence-based method, differed from the performance-based method by assigning vegetation types in the order from low to high prevalence. Thus the assembly methods worked in two principally different ways: the probability-based method assigned vegetation types to grid cells in a cell-by-cell manner, and both the performance-based method and prevalence-based method assigned them in a type-by-type manner. All methods were evaluated by use of reference data collected in the field, more or less independently of the data used to parameterize the vegetation-type models. Quantity, allocation, and total disagreement, as well as proportional dissimilarity metrics, were used for evaluation of assembly methods. Overlay analysis showed 38.1% agreement between all three assembly methods. The probability-based method had the lowest total disagreement with, and proportional dissimilarity from, the reference datasets, but the differences between the three methods were small. The three assembly methods differed strongly with respect to the distribution of the total disagreement on its quantity and allocation components: the cell-by-cell assignment method strongly favored allocation disagreement and the type-by-type methods strongly favored quantity disagreement. The probability-based method best reproduced the general pattern of variation across the study area, but at the cost of many rare vegetation types, which were left out of the assembled map. By contrast, the prevalence-based and performance-based methods represented vegetation types in accordance with nationwide area statistics. The results show that maps of vegetation types with wall-to-wall coverage can be assembled from individual distribution models with a quality acceptable for indicative purposes, but all the three tested methods currently also have shortcomings. The results also indicate specific points in the methodology for map assembly that may be improved.

Evaluation Carbonation Service Life of Mortar-Coated Concrete Considering Global Warming

Mortar finishing is frequently used to improve the carbonation service life of structural concrete. Moreover, carbonation is aggravated due to global warmings, such as the increase of CO2 concentration and temperature. This study shows a probability-based approach for evaluating the carbonation service life of coated concrete considering global warming. First, a carbonation model is proposed for assessing the carbonation depth of concrete with mortar finishing. The effect of global warming on carbonation is considered in the carbonation model. Second, a probability-based method is employed to determine the carbonation service life considering the thickness and mixtures of mortar finishing and substrate concrete. Based on the statistical analysis of calculation results, we find that for a concrete structural with 50 years’ service life, 15% service life will be reduced due to global warming.