Dynamic Statistical Model Research Articles

Dynamic statistical damage constitutive model based on the Hoek–Brown criterion at high strain rates

Dynamic statistical damage constitutive model based on the Hoek–Brown criterion at high strain rates

The 2023/24 El Niño and the Feasibility of Long-Lead ENSO Forecasting

Abstract Through its atmospheric teleconnections, El Niño–Southern Oscillation (ENSO) shifts and disrupts weather and climate patterns far beyond the equatorial Pacific where it occurs often resulting in catastrophic consequences in many countries of the world. It is also the largest source of seasonal and interannual climate predictability. Despite its huge importance, ENSO forecasting is still not performed operationally at longer leads than about 6 months ahead. At the same time, there is mounting scientific evidence that forecasts are possible even more than a year in advance. Early warning of ENSO events could substantially mitigate some of the most damaging impacts, such as floods, droughts, and harvest failure and help avoid famine, migration, and disease outbreaks. Here, we present forecasts from a statistical ENSO model of the next El Niño predicted to occur in the winter of 2023/24, at lead times between 11 and 17 months ahead of an expected peak in December 2023. We use a statistical unobserved dynamic components model (EDCM) based on subsurface ocean temperatures as well as sea surface temperatures and zonal wind stress. EDCM has been previously validated through hindcasts of the major El Niños since 1970 and through real-time forecasts of the 2015/16 and 2018/19 El Niños. Our statistical framework and results indicate that there is potential for doubling the operational predictive lead time of ENSO to at least 12 months, with additional promise for even earlier anticipation of 19 months. Such longer-lead forecasts could be of high value, because decision-making and management in a number of key socioeconomic sectors could be greatly improved.

A hybrid approach for skillful multiseasonal prediction of winter North Pacific blocking

Wintertime atmospheric blocking often brings adverse environmental and socioeconomic impacts through its accompanying temperature and precipitation extremes. However, due to the chaotic nature of the extratropical atmospheric circulation and the challenges in simulating blocking, the skillful seasonal prediction of blocking remains elusive. In this study, we leverage both observational data and seasonal hindcasts from a state-of-the-art seasonal prediction system to investigate the prediction skill of North Pacific wintertime blocking frequency and its linkage to downstream cold extremes. The observational results show that North Pacific blocking has a local maximum over the central North Pacific Ocean and that the occurrence of North Pacific blocking drives significant cold anomalies over northwestern North America within a week, which are both well reproduced by the model. The model skillfully predicts the western North Pacific blocking frequency near the subtropical jet exit region at the shortest forecast lead, but skill drops off rapidly with lead time partly due to model drift in the background flow. To overcome this rapid drop in skill, we develop a linear hybrid dynamical-statistical model that uses the forecasted Niño 3.4 index and upstream precipitation as predictors and that maintains significant forecast skill of high-latitude North Pacific blocking up to 7 lead months in advance. Our results indicate that an improvement in the seasonal prediction skill of winter North Pacific blocking frequency may be achieved by the enhanced representation of the links among sea surface temperature anomalies, tropical convection, and the ensuing tropical-extratropical interaction that initiates North Pacific blocking.

Harnessing science and technology policy to drive innovation in India: A conversation with Dr. Parvinder Maini

MIT Science Policy Review spoke with Dr. Parvinder Maini about the role of science and technology (S&T) policies in fostering innovation and entrepreneurship in India. Dr. Maini is the Scientific Secretary at the Office of the Principal Scientific Advisor (PSA) to the Government of India. In her current role, Dr. Maini is responsible for enlarging the R&D ecosystem in the country, formulating policies, and forging three-way industry-academia-government collaborations. A scientist-turned-policymaker, Dr. Maini has been instrumental in the launch of several critical national programs including the National Research Foundation and the National Deep Tech Policy among many others. An expert in meteorology and oceanography, Dr. Maini spent 20 years in the Ministry of Earth Sciences (MoES) where she was instrumental in the development and implementation of statistical dynamical models for providing location-specific forecasts. She also spearheaded the Atmospheric Science and International Cooperation programs at MoES Headquarters. We spoke with Dr. Maini about how the Office of the PSA synergistically works with other governmental policymaking institutions to achieve India’s S&T goals.

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling

Abstract This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts. Significance Statement The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

The impact of land use and cover changes on river flows in Wundanyi Catchment of Taita Hills, Kenya (1970–2030)

<p>Taita Hills are one of the most important biodiversity hotspots of Kenya but are experiencing a high rate of deforestation due to the to the conversion of its original forestland to agriculture and settlement during the last century. These landscape dynamics, coupled with rainfall fluctuations in these critical ecosystems, may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of land use/cover change (LUCC) in the Wundanyi catchment from 1970 to 2030 and predict their specific and combined effects on surface runoff and stream flow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary data from the Wundanyi catchment and Landsat TM and ETM+ imagery of Taita Hills for 1990, 2000, and 2010. Results show highly variable mean seasonal and annual values of discharge in Wundanyi catchment, probably attributed to environmental changes affecting Taita Hills in general and Wundanyi catchment in particular. Compared to 1990, major land use/cover changes in 2010 were featured by the expansion of built-up area (250%), plantation forest (23.7%), broadleaved forest (17.4%), and thicket (15.9%). It was also notable the decrease in woodland (−30.3%), cropland (−21.6%), and shrubland (−0.8%). Dynamic spatial trends by the year 2030 will be evidenced by increased thicket by 0.41% per annum (<em>R</em><sup>2</sup> = 81.6%) and by decreased plantation forests (−0.13%; <em>R</em><sup>2</sup> = 91.3%), woodland (−0.10%; <em>R</em><sup>2</sup> = 77.6%), shrubland (−0.11%; <em>R</em><sup>2</sup> = 85.2%), broadleaved forests (−0.03%; <em>R</em><sup>2</sup> = 56.6%) and cropland (−0.09%; <em>R</em><sup>2</sup> of 84.4). These changes will shape the catchment landscape and influence its hydrology, unless the existing forest and agricultural policy interventions are enforced. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi catchment.</p>

Integrative framework of multiple processes to explain plant productivity–richness relationships

Plant diversity and productivity, two crucial properties that sustain ecosystem structures, functions, and services, are intrinsically linked to numerous ecological fields, making productivity–richness relationships (PRR) a central ecological concern. Despite extensive research from the Darwinian era to the 21st century, the various shapes of PRR and their underlying theories have sparked ongoing debates. While several processes, theories, and integrative models have been proposed to explain PRR, a comprehensive understanding of the types of PRR, the effects of these processes on plant productivity and richness, and the relationships between PRR shapes remains elusive. This paper proposes a new integrative framework that focuses on these aspects, aiming to elucidate the diverse shapes of PRR and their interconnections. We review recent integrative methods that explain the roles of processes and the varying shapes in PRR to support this new framework. The paper traces the distinct phases in PRR research, including the discovery of PRR shapes, tests of influencing processes, and integrative research. We discuss the application of the Structural Equation Model (SEM), Statistical Dynamical Model (SDM), and Differential Dynamical Model (DDM) in integrative research. This integrative framework can guide theoretical and applied ecologists in identifying, deriving, explaining, and predicting the interconnected but distinct shapes of PRR. The humped, asymptotic, positive, negative, and irregular shapes of PRR are interconnected, with one shape potentially transforming into another. The balance between the positive and negative effects of different processes determines the different shapes of PRR, ultimately leading to a globally positive effect of plant diversity on plant productivity and other ecosystem functions.

Study of the stochastic dynamics of particles in the coal screening based on the Markov process

The model of the stochastic dynamics for the screening and screen-penetration processes was developed based on the Markov process to reveal the mechanism of the coal screening effect. As a partial differential equation, the model of the stochastic dynamics describes the relationship between particle position in the y direction, screening time, and particle screen-penetration probability. The obtained model for stochastic dynamics can calculate the screen-penetration probability of a specific particle size at any given time. Moreover, two coefficients, a1 and a2, are defined where a1 represents the average velocity of particles, and a2 represents the combined effect of orderly segregation and mixing of particles in chaotic motion. These coefficients of the stochastic dynamics model were obtained by regression analysis. Additionally, the statistical analysis showed that the fitting errors were all <1. The proposed statistical dynamics model works for all particle sizes. A necessary coefficient, a1, was calculated based on screen-penetration coefficients k, a, and b. A large value of a1 means a high average velocity of particles and a greater screen-penetration probability. The value of a1 for particles smaller than 6 mm declines as the feed mass increases. Moreover, it initially decreases, followed by an increase, in response to a rising proportion of particles <6 mm. In contrast, the a1 value for particles ranging between 6 mm and 13 mm undergoes minimal changes when subjected to variations in feed mass and the proportion of particles <6 mm.

Operational scheme for predicting tropical cyclone wind radius based on a statistical–dynamical approach and track pattern clustering

AbstractAn operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical–dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5‐kt wind radius (R5)—a proxy of TC size—relative to the initial time. The performance of the model was compared for the training (2008–2016) and testing (2017–2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non‐clustering and clustering models. The clustering model improved the prediction of TC size by 3%–24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real‐time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%–19% less than that of the non‐clustered model. The analysis results revealed that the real‐time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase.

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

Bayesian brain computing and the free-energy principle: an interview with Karl Friston.

The free-energy principle entails the Bayesian brain hypothesis that can be implemented by many schemes considered in this field. The combination of multimodal brain imaging and free-energy minimization has shown promise in unraveling complex brain dynamics and understanding the interactions among distinct brain regions. The Bayesian mechanics of brain computing gives a unique route to understanding authentic (neuromimetic) intelligence and, more importantly, points towards the development of brain-inspired intelligence. NSR spoke to a leading theoretical neuroscientist and authority on brain imaging-Karl Friston, the inventor of statistical parametric mapping, voxel-based morphometry and dynamic causal modeling. Friston is also known for his contributions to theoretical biology in the form of the free-energy principle and applications such as active inference. Friston is currently the Scientific Director of the Wellcome Trust Centre for Neuroimaging, Professor of Neuroscience at Queen Square Institute of Neurology, University College London and Honorary Consultant at The National Hospital for Neurology and Neurosurgery, UK.

Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions

Abstract. There is a scientific consensus that the Mediterranean region (MedR) is warming and as the temperature continues to rise, droughts and heat waves are becoming more frequent, severe, and widespread. Given the detrimental effects of droughts, it is crucial to accelerate the development of forecasting and early warning systems to minimize their negative impact. This paper reviews the current state of drought modeling and prediction applied in the MedR, including statistical, dynamical, and hybrid statistical–dynamical models. By considering the multifaceted nature of droughts, the study encompasses meteorological, agricultural, and hydrological drought forms and spans a variety of forecast scales, from weekly to annual timelines. Our objective is to pinpoint the knowledge gaps in literature and to propose potential research trajectories to improve the prediction of droughts in this region. The review finds that while each method has its unique strengths and limitations, hybrid statistical–dynamical models appear to hold the most promising potential for skillful prediction with seasonal to annual lead times. However, the application of these methods is still challenging due to the lack of high-quality observational data and the limited computational resources. Finally, the paper concludes by discussing the importance of using a combination of sophisticated methods such as data assimilation techniques, machine learning models, and copula models and of integrating data from different sources (e.g., remote sensing data, in situ measurements, and reanalysis) to improve the accuracy and efficiency of drought forecasting.

Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters

In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.

Statistical-dynamical modeling of the maize yield response to future climate change in West, East and Central Africa using the regional climate model REMO

Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982–2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.

A Rapid Intensification Deterministic Ensemble (RIDE) for the Joint Typhoon Warning Center’s Area of Responsibility

Abstract The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s area of responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms. Significance Statement An operational tropical cyclone forecaster makes a forecast with deterministic and probabilistic intensity guidance tools at their disposal. These models have a varying degree of abilities for predicting both intensity change and rapid intensification. The forecaster faces a dilemma in how to combine this disparate guidance to anticipate rapid intensification events. Here, the RIDE model provides probability forecasts associated with rapid intensification at 12-, 24-, 36-, 48-, and 72-h lead times and associated deterministic forecasts. RIDE provides skillful rapid intensification forecasts and helps rectify this forecast dilemma.

Optimizing solar-assisted industrial heating and cooling system for cost-effective installation

Integrating solar thermal collectors into industrial processes could be a viable way to replace the use of conventional fuels and achieve economic and environmental goals. However, there is a need to consider the detailed dynamic operation of a system with storage on a systematic control scale to fully optimize realistic system performance under variable conditions by minimizing excess energy production and maximizing annual life-cycle cost savings. In this study, we developed a TRNSYS-based dynamic statistical optimization model and evaluated FPC-based solar-assisted heating systems to develop a cost-effective system design for two industries: MOHA soft drinks and Sheba leather factories in the Tigray region, Ethiopia. Three operating loads were compared: process heat, utility heat, and utility heat and chilled water. The optimized designs resulted in significant annual life-cycle cost savings, high solar fractions, and a good margin on temperature trends where solar collector size has a greater impact. Annual cost savings per unit area of solar collector for process and utility heat were in the range of $51–90/m2 for a collector mass flow rate and storage volume of 0.01–0.02 m3/h-m2 and 0.04–0.08 m3/m2, respectively. For the utility heat and chilled water loads, the values were $49/m2 for a mass flow rate of 0.04 m3/h-m2 and a storage volume of 0.07 m3/m2. Thus, the study supports the transient analysis of solar-assisted industrial heat. The case studies have shown that the method provides optimal solutions for the use of solar thermal energy. As investment and financial sourcing remain a priority challenge, the model and case study results could help in decision-making for similar and other production capacities, regions, industries, and solar technologies.

Data driven pathway analysis and forecast of global warming and sea level rise

Climate change is a critical issue of our time, and its causes, pathways, and forecasts remain a topic of broader discussion. In this paper, we present a novel data driven pathway analysis framework to identify the key processes behind mean global temperature and sea level rise, and to forecast the magnitude of their increase from the present to 2100. Based on historical data and dynamic statistical modeling alone, we have established the causal pathways that connect increasing greenhouse gas emissions to increasing global mean temperature and sea level, with its intermediate links encompassing humidity, sea ice coverage, and glacier mass, but not for sunspot numbers. Our results indicate that if no action is taken to curb anthropogenic greenhouse gas emissions, the global average temperature would rise to an estimated 3.28 °C (2.46–4.10 °C) above its pre-industrial level while the global sea level would be an estimated 573 mm (474–671 mm) above its 2021 mean by 2100. However, if countries adhere to the greenhouse gas emission regulations outlined in the 2021 United Nations Conference on Climate Change (COP26), the rise in global temperature would lessen to an average increase of 1.88 °C (1.43–2.33 °C) above its pre-industrial level, albeit still higher than the targeted 1.5 °C, while the sea level increase would reduce to 449 mm (389–509 mm) above its 2021 mean by 2100.

Modeling distribution parameters in system dynamics: an application in childhood obesity

AbstractSystem dynamics models are increasingly being used to understand the underlying dynamics of populations and hypothesize causal system structures that can account for changes in a population's disease burden. A considerable challenge for public health modeling is understanding how changes in underlying determinants of a problem impact a population's measure of public health, such as the prevalence of a disease. Additionally, it is common to have limited insights and data for the dynamics of these determinants. This article presents an analytical method of estimating underlying distributions using model‐generated prevalence relying on a minimum number of model stratum. This method models the evolution of the underlying distribution parameters by combining statistical distribution theory and system dynamics models. This article provides general equations for various applications and an in‐depth example using body mass index. © 2023 The Authors. System Dynamics Review published by John Wiley &amp; Sons Ltd on behalf of System Dynamics Society.

A multivariate dynamic statistical model of the global carbon budget 1959–2020

AbstractWe propose a multivariate dynamic statistical model of the global carbon budget (GCB) as represented in the annual data set made available by the Global Carbon Project, covering the sample period 1959–2020. The model connects four main objects of interest: atmospheric carbon dioxide (CO2) concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink), and by the ocean and marine biosphere (ocean sink). The model captures the GCB equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World Gross Domestic Product while sink activities depend on the level of atmospheric concentrations and the Southern Oscillation Index. We derive the time series properties of atmospheric concentrations from the model, showing that they contain one unit root and a near-second unit root. The statistical system allows for the estimation of key parameters of the global carbon cycle and for the assessment of estimation uncertainty. It also allows for the estimation and the uncertainty assessment of related variables such as the airborne fraction and the sink rate. We provide short-term forecasts of the components of the GCB.

Mechanical properties, damage evolution and energy dissipation of cemented tailings backfill under impact loading

In order to explore the influence of average strain rate(ASR) on mechanical properties, damage evolution and energy dissipation of cemented tailings backfill(CTB), the dynamic test of CTB was carried out in the laboratory. This study shows that dynamic peak stress and initial crack stress of CTB increase exponentially with the increase of ASR, indicating that the characteristic stress of CTB has a significant strain rate effect. The dynamic peak strain and initial crack strain of CTB also increase exponentially with the increase of ASR, and the CTB with lower cement content is more likely to produce deformation and failure. The increase of ASR can improve the energy indexes of CTB at the peak stress point, and the increase of cement content can also improve the energy parameters of CTB. The energy storage limit of CTB increases exponentially with the increase of ASR. In addition, a dynamic statistical damage constitutive model of CTB was established considering ASR, and the accuracy and reliability were verified. There are three stages for the damage failure of CTB: rapid growth stage of damage, slow growth stage of damage and stable growth stage of damage. In addition, the high strain rate has a certain retarding effect on the development of cracks inside the CTB, thus inhibiting the rapid growth of damage value. Under impact loading, the CTB samples are mainly axial macroscopic main crack tensile failure and crushing failure. When the average strain rate is higher than the critical strain rate, the failure pattern of CTB is mainly crushing failure. When the average strain rate is basically the same, the damage degree of CTB with higher cement content is smaller.