scholarly journals Understanding Mesoscopic Chemo-Mechanical Distress and Mitigation Mechanisms of Concrete Subject to ASR

Author(s):  
Md Asif Rahman

Alkali-silica reaction (ASR) is one of the common sources of concrete damage worldwide. The surrounding environment, namely, temperature and humidity greatly influence the alkali-silica reaction induced expansion. Global warming (GW) has caused frequent change in the climate and initiated extreme weather events in recent years. These extreme events anticipate random change in temperature and humidity, and convey potential threats to the concrete infrastructure. Moreover, external loading conditions also affect the service life of concrete. Thus, complex mechanisms of ASR under the impact of seasonal change and global warming require a precise quantitative assessment to guide the durable infrastructure materials design practices. Despite decades of phenological observation study, the expansion behavior of ASR under these situations remains to be understood for capturing the ASR damage properly. Within this context this research focuses on the mathematical model development to quantify and mitigate ASR-induced damage. Mesoscale characteristics of ASR concrete was captured in the virtual cement-concrete lab where the ASR gel-induced expansion zone was added as a uniform thickness shell. Finite element method (FEM) was used to solve the ASR formation and expansion evolution. The results of this study are presented in the form of one conference and their journal manuscripts. The first manuscript focuses on the development of the governing equations based on the chemical formulas of alkali-silica reaction to account for the ASR kinetics and swelling pressure exerted by the ASR expansion. There is a fluid flow and mass transfer in the concrete domain due to ASR gel associated from ASR kinetics. This paper involves derivation of the mass and momentum balance equation in terms of the thermo-hygro-mechanical (THM) model. THM model accounts for thermal expansion and hygroscopic swelling in addition to traffic loads to represent volumetric change in the concrete domain. The second manuscript is a case study based on different cement-aggregate proportions and alkali hydroxide concentrations. It is important to know how ASR evolves under variable concentration of the chemical species. The simulated results show that high concentration of hydroxide ion in concrete initiates more reaction and damage in concrete. Also chemical reaction moves to the right direction with low cement to aggregate ratio which means ASR expansion depends on the availability of the reactive aggregates in the concrete domain. The third manuscript attempts to develop a simplified ASR model that integrates chemo-physio-mechanical damage under stochastic weather impact. Stochasicity incorporates the random behavior of surrounding nature in the model. The simulated results elucidate that ASR expansion is more severe under the influence of global warming and climate change. This will support long-term damage forecasts of concrete subjected to extreme weather events. The fourth manuscript focuses on the quantification of mechanical damage under ASR expansion and a dedicated mitigation scheme to minimize it. Added creep loads and physics identify the role of creep damage on ASR expansion. The results from this paper confirms that the ASR-induced damage significantly minimize the load carrying capacity of concrete. It directly affects the compressive strength, tensile strength, and modulus of elasticity of concrete. Damage in aggregates domain is more than the mortar phase under the creep loadings. Among many supplementary materials, fly ash is the most effective in minimizing ASR expansion and damage. This work also includes a petrographic comparison between different mineral types collected from different locations to identify the reactivity of certain aggregates. Thus, the final outcome of this research is a complete model which is a conclusive solution to the long-term ASR damage prediction. The validated model provides better understanding of ASR kinetics from mesoscale perspective. The developed model can potentially accelerate the precise prediction of concrete service life and mitigation schemes as well as can be used as an alternative scope to the costly laboratory tests methods.

2019 ◽  
Vol 25 (4) ◽  
pp. 189-190
Author(s):  
Kent E. Pinkerton ◽  
Emily Felt ◽  
Heather E. Riden

Abstract. A warming climate has been linked to an increase in the frequency and severity of extreme weather events, including heat and cold waves, extreme precipitation, and wildfires. This increase in extreme weather results in increased risks to the health and safety of farmworkers. Keywords: Climate change, Extreme weather, Farmworkers, Global warming, Health and safety.


2018 ◽  
Vol 2 (1) ◽  
pp. 9-24
Author(s):  
Edoardo Bertone ◽  
Oz Sahin ◽  
Russell Richards ◽  
Anne Roiko

Abstract A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.


Author(s):  
Christopher P. Borick ◽  
Barry G. Rabe

The factors that determine individual perceptions of climate change have been a focus of social science research for many years. An array of studies have found that individual-level characteristics, such as partisan affiliation, ideological beliefs, educational attainment, and race, affect one’s views on the existence of global warming, as well as the levels of concern regarding this matter. But in addition to the individual-level attributes that have been shown to affect perceptions of climate change, a growing body of literature has found that individual experiences with weather can shape a variety of views and beliefs that individuals maintain regarding climate change. These studies indicate that direct experiences with extreme weather events and abnormal seasonal temperature and precipitation levels can affect the likelihood that an individual will perceive global warming to be occurring, and in some cases their policy preferences for addressing the problem. The emerging literature on this relationship indicates that individuals are more likely to express skepticism regarding the existence of global warming when experiencing below average temperatures or above average snowfall in the period preceding an interview on their views. Conversely, higher temperatures and various extreme weather events can elevate acceptance of global warming’s existence. A number of studies also find that individuals are more likely to report weather conditions such as drought and extreme heat affected their acceptance of global warming when such conditions were occurring in their region. For example, the severe drought that has encompassed much of the western United States between 2005 and 2016 has increasingly been cited by residents of the region as the primary reason for their belief that climate change is occurring. What remains unclear at this point is whether the weather conditions are actually changing opinions regarding climate change or if the preexisting opinions are causing individuals to see the weather events in a manner consistent with those opinions. Notably, the relationship between weather experiences and beliefs regarding climate change appear to be multidirectional in nature. Numerous studies have found that not only do weather experiences shape the views of individuals regarding global warming, but also individuals’ views on the existence of global warming can affect their perceptions of the weather that they have experienced. In particular, recent research has shown that individuals who are skeptical about the existence of global warming are less likely to report the weather recorded in their area accurately than individuals who believe global warming is happening.


2013 ◽  
Vol 72 (2) ◽  
pp. 30 ◽  
Author(s):  
Külli Kangur ◽  
Peeter Kangur ◽  
Kai Ginter ◽  
Kati Orru ◽  
Marina Haldna ◽  
...  

2013 ◽  
Vol 17 (5) ◽  
pp. 2069-2081 ◽  
Author(s):  
T. A. Räsänen ◽  
C. Lehr ◽  
I. Mellin ◽  
P. J. Ward ◽  
M. Kummu

Abstract. Globally, there have been many extreme weather events in recent decades. A challenge has been to determine whether these extreme weather events have increased in number and intensity compared to the past. This challenge is made more difficult due to the lack of long-term instrumental data, particularly in terms of river discharge, in many regions including Southeast Asia. Thus our main aim in this paper is to develop a river basin scale approach for assessing interannual hydrometeorological and discharge variability on long, palaeological, time scales. For the development of the basin-wide approach, we used the Mekong River basin as a case study area, although the approach is also intended to be applicable to other basins. Firstly, we derived a basin-wide Palmer Drought Severity Index (PDSI) from the Monsoon Asia Drought Atlas (MADA). Secondly, we compared the basin-wide PDSI with measured discharge to validate our approach. Thirdly, we used basin-wide PDSI to analyse the hydrometeorology and discharge of the case study area over the study period of 1300–2005. For the discharge-MADA comparison and hydrometeorological analyses, we used methods such as linear correlations, smoothing, moving window variances, Levene type tests for variances, and wavelet analyses. We found that the developed basin-wide approach based on MADA can be used for assessing long-term average conditions and interannual variability for river basin hydrometeorology and discharge. It provides a tool for studying interannual discharge variability on a palaeological time scale, and therefore the approach contributes to a better understanding of discharge variability during the most recent decades. Our case study revealed that the Mekong has experienced exceptional levels of interannual variability during the post-1950 period, which could not be observed in any other part of the study period. The increased variability was found to be at least partly associated with increased El Niño Southern Oscillation (ENSO) activity.


Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Huifen Zhou ◽  
Huiying Ren ◽  
Patrick Royer ◽  
Hongfei Hou ◽  
Xiao-Ying Yu

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.


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