520 research outputs found

    Data-driven method for enhanced corrosion assessment of reinforced concrete structures

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    Corrosion is a major problem affecting the durability of reinforced concrete structures. Corrosion related maintenance and repair of reinforced concrete structures cost multibillion USD per annum globally. It is often triggered by the ingression of carbon dioxide and/or chloride into the pores of concrete. Estimation of these corrosion causing factors using the conventional models results in suboptimal assessment since they are incapable of capturing the complex interaction of parameters. Hygrothermal interaction also plays a role in aggravating the corrosion of reinforcement bar and this is usually counteracted by applying surface protection systems. These systems have different degree of protection and they may even cause deterioration to the structure unintentionally. The overall objective of this dissertation is to provide a framework that enhances the assessment reliability of the corrosion controlling factors. The framework is realized through the development of data-driven carbonation depth, chloride profile and hygrothermal performance prediction models. The carbonation depth prediction model integrates neural network, decision tree, boosted and bagged ensemble decision trees. The ensemble tree based chloride profile prediction models evaluate the significance of chloride ingress controlling variables from various perspectives. The hygrothermal interaction prediction models are developed using neural networks to evaluate the status of corrosion and other unexpected deteriorations in surface-treated concrete elements. Long-term data for all models were obtained from three different field experiments. The performance comparison of the developed carbonation depth prediction model with the conventional one confirmed the prediction superiority of the data-driven model. The variable importance measure revealed that plasticizers and air contents are among the top six carbonation governing parameters out of 25. The discovered topmost chloride penetration controlling parameters representing the composition of the concrete are aggregate size distribution, amount and type of plasticizers and supplementary cementitious materials. The performance analysis of the developed hygrothermal model revealed its prediction capability with low error. The integrated exploratory data analysis technique with the hygrothermal model had identified the surfaceprotection systems that are able to protect from corrosion, chemical and frost attacks. All the developed corrosion assessment models are valid, reliable, robust and easily reproducible, which assist to define proactive maintenance plan. In addition, the determined influential parameters could help companies to produce optimized concrete mix that is able to resist carbonation and chloride penetration. Hence, the outcomes of this dissertation enable reduction of lifecycle costs

    Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions

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    Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research

    Impact of ETICS on Corrosion Propagation of Concrete Facade

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    AbstractThe durability of reinforced concrete facades is an important field of research as the majority of dwellings in Northern and Eastern Europe were constructed 30–50 years ago. Recent condition assessments of the façades have indicated damage related to carbonation induced corrosion. Moreover, the problem might escalate since the future climate scenarios predict a significant increase of CO2 in ambient air being a driving force for carbonation.Assessment of residual service life of concrete facades is a complex phenomenon with a high level of uncertainty. A validated method used in this study combines dynamic hygrothermal simulation tool Delphin and existing corrosion models. Corrosion propagation consists of the time needed to concrete cover cracking and further expansion of a crack up to a width of 0.3mm as a limit criterion. Additional exterior thermal insulation (mostly ETICS) is applied to existing dwellings as a renovation scenario in order to decrease the heat loss, improve thermal comfort and prevent the degradation mechanism e.g. carbonation induced corrosion. Hence, reinforcement corrosion before and after installing ETICS with mineral wool, EPS or PIR has to be evaluated. Impact of boundary conditions, e.g. wind-driven rain in addition to material properties, and built-in moisture was included.The results indicate that corrosion propagation after carbonation has reached the reinforcement, is three to six years depending on the ratio of concrete cover depth against the reinforcement diameter. While applying ETICS, corrosion accelerates for a short period of time up to one year. Temperature inside the wall rises above +10°C throughout the year, meaning no more freeze-thaw damage. Corrosion of reinforcement in carbonated concrete after applying ETICS is so slow, that no cracking will develop. Drying out moisture or vapour diffusion from indoor air is not able to propagate corrosion of reinforcement in carbonated concrete

    Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods

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    This study investigates the feasibility of introducing machine learning algorithms to predict the diffusion resistance to chloride penetration of recycled aggregate concrete (RAC). A total of 226 samples collated from published literature were used to train and test the developed machine learning framework, which integrated four standalone models and two ensemble models. The hyperparameters involved were fine-tuned by grid search and 10-fold cross-validation. Results showed that all the models had good performance in predicting the chloride penetration resistance of RAC and among them, the gradient boosting model outperformed the others. The water content was identified as the most critical factor affecting the chloride ion permeability of RAC based on the standardized regression coefficient analysis. The model’s interpretability was greatly improved through a two-way partial dependence analysis. Finally, based on the proposed machine learning models, a performance-based mixture design method and a service life prediction approach for RAC were developed, thereby offering novel and robust design tools for achieving more durable and resilient development goals in procuring sustainable concrete.This work was supported by the National Natural Science Foundation of China (52108123), Guangdong Basic and Applied Basic Research Foundation (2020A1515110101), and Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology (2021B1212040003)

    Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

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    Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

    Investigation into reliability based methods to include risk of failure in life cycle cost analysis of reinforced concrete bridge rehabilitation

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    Reliability based life cycle cost analysis is becoming an important consideration for decision-making in relation to bridge design, maintenance and rehabilitation. An optimal solution should ensure reliability during service life while minimizing the life cycle cost. Risk of failure is an important component in whole of life cycle cost for both new and existing structures. Research work presented here aimed to develop a methodology for evaluation of the risk of failure of reinforced concrete bridges to assist in decision making on rehabilitation. Methodology proposed here combines fault tree analysis and probabilistic time-dependent reliability analysis to achieve qualitative and quantitative assessment of the risk of failure. Various uncertainties are considered including the degradation of resistance due to initiation of a particular distress mechanism, increasing load effects, changes in resistance as a result of rehabilitation, environmental variables, material properties and model errors. It was shown that the proposed methodology has the ability to provide users two alternative approaches for qualitative or quantitative assessment of the risk of failure depending on availability of detailed data. This work will assist the managers of bridge infrastructures in making decisions in relation to optimization of rehabilitation options for aging bridges

    Life Cycle Assessment on Green Building Implementation

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    Greenhouse-gas emissions have become one of the most impacting environmental issues in today’s society. A rapidly increasing trend in global CO2emissions particularly since the early nineties (23.64% since 1990) has led to the generation of about 50,000 million tons of CO2–equivalent (eqv) worldwide in 2010. According to mainstream climate experts, the increasing concentration of greenhouse-gases is having a warming effect on the world climate. To slow down global warming, there is a global focus on reducing greenhouse-gas emissions. Life cycle assessment in green building implementation is the focus of this Special Issue
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