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

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

Suitability Investigation of Recycled Concrete Aggregates for Concrete Production : An Experimental Case Study

In developing countries, construction and demolition waste (CDW) is disposed to landfill, causing social, environmental, and economic crises. In these nations, CDW exponentially increase due to their rapid economic growth, industrialization, and urbanization. This paper aims to examine the possibility of recycling concrete waste for production of new concrete in Ethiopia. Physical and mechanical characteristics of recycled concrete aggregate (RCA) acquired from concrete waste are thoroughly examined. Though the RCA exhibited relatively lesser performance compared with the natural coarse aggregate (NCA), it reveals the same properties as of normal-weight aggregates in several instances. The performance of concrete specimens which employ RCA up to 20% is evaluated from workability, strength, and permeability aspects. The utilization of RCA slightly affects the workability and the water permeability properties of the concretes. Replacement of 10% of the NCA by the RCA enhances the compressive strength of the hardened concrete by 8%. The difference between the splitting tensile strength of the concretes which employ RCA and conventional aggregates is trivial. Generally, this work demonstrates the practicability of concrete waste recycling to produce new concrete or construction materials in Ethiopian context.Peer reviewe

Machine learning for assessing chloride resistance of concrete

Predicting the chloride resistance property of concrete accurately is critical in structural engineering. This thesis project adopts a state-of-the-art machine learning algorithm, XGBoost, to predict the chloride migration coefficient (Dnssm) of concrete. An extensive database of experimental data covering various concrete types has been compiled from research projects and previously published studies. Depending on the number and type of input features, four Dnssm prediction models are developed. All models are verified with unseen data using four statistical performance indicators and compared to other five tree-based algorithms, which are Decision Tree, Random Forest, AdaBoost, Gradient Boosting, and Bagging. The verification results confirm that the XGBoost model accurately predicts the Dnssm. The model is indispensable in practice as engineers around the world can use it to assess the performance of newly designed concrete against chloride resistance. It also has economic implications as it helps to design durable concrete mixes without the need for time-consuming and resource-intensive advanced laboratory testing. The model could also improve environmental performance by reducing precautionary overdesign of concrete properties and saving natural resources that would otherwise be wasted, and thus contributing to the achievement of the Sustainable Development Goal (SDG 13)

Autonomous corrosion assessment of reinforced concrete structures : Feasibility study

In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl−) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl− sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl− wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.Peer reviewe

Service Life Prediction of Repaired Structures Using Concrete Recasting Method: State-Of-The-Art

The performance of repaired concrete structures continues to be a major global concern. Regardless of improvements in repairing materials and methods, several repaired concrete structures still fail prematurely, leading to costly and time consuming repairs of repairs. Studies in the field of concrete repairs showed that almost 50% of repaired concrete structures had failed in Europe and USA. Simultaneously, numerous existing concrete structures needs to be repaired as they do not meet today's safety standard. As a result, annually billions of EUR will continue to be spent in order to repair deteriorated concrete structures. The need to mitigate premature failure of repaired concrete structures has to inspire many researchers to develop service life prediction model for repaired concrete structures. However, till today, service life of repaired concrete structures is just an estimate which relies on individual's experience. Scientifically developed service life prediction model for repaired concrete structuresis highly desired for optimizing selection of repairing materials and techniques in turn diminishing economic loss due to premature repaired concrete failure. The aims of this paper is generally to review the performance of repaired concrete structures and the current status in the development of service life prediction models for repaired concrete structures specially exposed to exposure class XD (chlorides excluding seawater). Future research and development of service life prediction model for repaired concrete structures is discussed based on today's research and practice on the area.Peer reviewe

Embodied energy and CO2 emissions of widely used building materials : The Ethiopian context

Buildings use a wide range of construction materials, and the manufacturing of each material consumes energy and emits CO2. Several studies have already been conducted to evaluate the embodied energy and the related CO2 emissions of building materials, which are mainly based on case studies from developed countries. There is a considerable gap in cases of developing countries regarding assessment of embodied energy and CO2 emissions of these building materials. This study identified the top five most used construction materials (cement, sand, coarse aggregates, hollow concrete blocks, and reinforcement bars), which are also prime sources of waste generation during construction in the Ethiopian building construction sector. Then, what followed was the evaluation of the embodied energies and CO2 emissions of these materials by examining five commercial and public buildings within the cradle-to-site lifecycle boundary. The evaluation results demonstrated that cement, hollow concrete blocks (HCB), and reinforcement bars (rebars) are the major consumers of energy and major CO2 emitters. Cumulatively, they were responsible for 94% of the embodied energy and 98% of the CO2 emissions. The waste part of the construction materials has inflated the embodied energy and the subsequent CO2 emissions considerably. The study also recommended several strategies for the reduction of embodied energy and the related CO2 emissions. The research delivers critical insights into embodied energy and CO2 emissions of the five most used building materials in the Ethiopian construction industry, as there are no prior studies on this theme. This might be a cause to arouse awareness and interest among the policy makers and the wider public to clearly understand the importance of research on this crucial issue to develop national energy and CO2 descriptors for construction materials, in order to take care of our naturally endowed, but yet fragile, human habitat.Peer reviewe