Measurement of Reinforcement Corrosion in Concrete Adopting Ultrasonic Tests and Artificial Neural Network

Limited research has been performed in testing and measuring the reinforcement corrosion levels using non-destructive tests. This research applied ultrasonic-based non-destructive test and artificial neural network to the diagnosis and prediction of rebar’s non-uniform corrosion-induced damage within reinforced concrete members. Ultrasonic velocities were tested by applying ultrasonic to reinforced concrete prisms before and after the rebar corrosion. Input parameters including concrete strength, ultrasonic velocity, and the specimen dimension-related variable were used for the prediction of reinforcement corrosion level adopting artificial neural network models. Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model. This study leads to future research in high-accuracy non-destructive measurement of reinforcement corrosion in concrete

A system for crack pattern detection, characterization and diagnosis in concrete structures by means of image processing and machine learning techniques

A system that attempts to find cracks in a RGB picture of a concrete beam, measure the cracks angles and widths; and classify crack patterns in 3 pathologies has been designed and implemented in the MATLAB programming language. The system is divided in three parts: Crack Detection, Crack Clustering and Crack Pattern Classification. The Crack Detection algorithm attempts to detect pixels depicting cracks in a region of interest (ROI) and measure the crack angles and widths. The input ROI is segmented several times: First with an artificial Neural Network (NN) that classifies image patches in "Crack" or "Not Crack", then with the Canny Edge detector and finally with the local Mean and Standard deviation of the intensities. Then all neighborhoods in the mask are passed through special modified line kernels called "orientation kernels" designed to detect cracks and measure their angles; in order to obtain the width measurement, a line of pixels perpendicular to the crack is extracted and with an approximation of the intensity gradient of that line the width is measured. This algorithm outputs a mask the same size as the input picture with the measured angles and widths. The Crack Clustering algorithm groups up all the crack image patches recognized from the Crack Detection to approximate clusters that match the quantity of cracks in the image. To achieve this a special distance metric has been designed to group up aligned crack image patches; then with an algorithm based on the connectivity among the crack patches the clusters are obtained. The Crack Pattern Classification takes the mask outputs from the Crack Detection step as input for a Neural Network (NN) designed to classify crack patterns in concrete beams in 3 classes: Flexion, Shear and Corrosion-Bond cracks. The width and angles masks are first transformed into a Feature matrix to reduce the degrees of freedom of the input for the NN. To achieve a desirable classification in cases when more than 1 pathology is present, every angle and width mask is separated in as many Features matrices as clusters found with the Clustering algorithm; then separately classified with the NN designed. Several photos depicting concrete surfaces are presented as examples to check the accuracy of the width and angle measurements from the Crack Detection step. Other photos showing concrete beams with crack patterns are used to check the classification prowess of the Crack Pattern Classification step. The most important conclusion of this work is the transference of empirical knowledge from rehabilitation of structures to a machine learning model in order to diagnose the damage on an element. This opens possibilities for new lines of research to make a larger system with wider utilities, more pathologies and elements to classify.Se ha diseñado un sistema que a partir de una foto a color de una superficie de hormigón realiza las siguientes tareas: Detectar fisuras, medir su ángulo y ancho, clasificar los patrones de fisuración asociados a tres patologías del hormigón; el cual ha sido implementado en el lenguaje de programación MATLAB. El sistema se divide en tres partes: Detección y medición de fisuras; algoritmo de análisis de grupos de fisuras y clasificación de patrones de fisuración. El algoritmo de detección de fisuras detecta los pixeles en donde hay fisuras dentro de una región de interés y mide el ancho y ángulos de dichas fisuras. La región de interés es segmentada varias veces: Primero con una red neuronal artificial que clasifica teselas de la imagen en dos categorías "Fisura" y "No fisura"; después se hace otra segmentación con un filtro Canny de detección de bordes y finalmente se segmenta con la media y desviaciones intensidades en teselas de la imagen. Entonces todas las localidades de la máscara de imagen obtenida con las segmentaciones anteriores se las pasa por varios filtros de detección de líneas diseñados para detectar y medir las fisuras. Este algoritmo resulta en dos máscaras de imagen con los anchos y ángulos de todas las fisuras encontradas en la región de interés. El algoritmo de análisis de grupos de teselas reconocidas como fisuras se hace para intentar reconocer y contar cuantas fisuras aparecen en la región de interés. Para lograr esto se diseñó una función de distancia para que teselas de fisura alineadas se junten; después con un algoritmo basado en la conectividad entre estas teselas o vectores fisura se obtienen los grupos de fisura. La clasificación de patrones de fisuración toma las máscaras de imagen del paso de detección de fisuras y lo toma como dato de entrada para una red neuronal diseñada para clasificar patrones de fisuración en tres categorías seleccionadas: Flexión, Cortante y Corrosión-Adherencia. Las máscaras de imagen de ancho y ángulo se transforman en una matriz de características para reducir los grados de libertad del problema, estandarizar un tamaño para la entrada al modelo de red neuronal. Para lograr clasificaciones correctas cuando más de 1 patología está presente en las vigas, cada máscara de imagen de ángulos y anchos de fisura se divide en cuantos cuantos grupos de teselas de fisuras haya en la imagen, y para cada uno se obtienen una matriz de características. Entonces se clasifican separadamente dichas matrices con la red neuronal artificial diseñada. Varias fotos con superficies de hormigón se presentan como ejemplos para evaluar la precisión de las mediciones de ancho y ángulo del paso de detección de fisuras. Otras fotos mostrando patrones de fisuración en vigas de hormigón se muestran para revisar las capacidades de diagnóstico del paso de clasificación de patrones de fisuración. La conclusión más importante de este trabajo es la transferencia del conocimiento empírico de la rehabilitación de estructuras hacia un modelo de inteligencia artificial para diagnosticar el daño en un elemento de la estructura. Esto abre un campo grande de líneas de investigación hacia el diseño e implementación de sistemas automatizados con más utilidades, más patologías y elementos para clasificar.Postprint (published version

Bond Performance between Corroded Steel and Recycled Aggregate Concrete Incorporating Nano Silica

The current research project mainly aims to investigate the corrosion resistance and bond performance of steel reinforced recycled aggregate concrete incorporating nano-silica under both normal and corrosive environmental conditions. The experimental part includes testing of 180 pull-out specimens prepared from 12 different mixtures. The main parameters studied were the amount of recycled aggregate (RCA) (i.e. 0%, 25%, 50% and 100%), nano silica (1.5% and 3%), steel embedment length as well as steel bar diameter (12 and 20mm). Different levels of corrosion were electrochemically induced by applying impressed voltage technique for 2, 5, 10 and 15 days. The experimental observations mainly focused on the corrosion level in addition to the ultimate bond, failure modes and slips occurred. Experimental results showed that the bond performance between un-corroded steel and recycled aggregate concrete slightly reduced, while a significant degradation was observed after being exposed to corrosive conditions, in comparison to normal concrete. On the other hand, the use of nano silica (NS) showed a reasonable bond enhancement with both normal and RCA concretes under normal conditions. However, much better influence in terms of bond and corrosion resistance was observed under advancing levels of corrosion exposure, reflecting the improvement in corrosion resistance. Therefore, NS was superbly effective in recovering the poor performance in bond for RCA concretes. More efficiency was reported with RCA concretes compared to the conventional concrete. The bond resistance slightly with a small amount of corrosion (almost 2% weight loss), then a significant bond degradation occurs with further corrosion. The influence of specific surface area and amount of nano silica on the performance of concrete with different water/binder (w/b) ratios has been also studied, using 63 different mixtures produced with three different types of colloidal NS having various surface areas and particle sizes. The results showed that the performance of concrete is heavily influenced by changing the surface area of nano silica. Amongst the three used types of nano silica, NS with SSA of 250 m2 /g achieved the highest enhancement rate in terms of compressive strength, water absorption and microstructure analysis, followed by NS with SSA of 500 m2/g, whilst NS with SSA of 51.4 m2 /g was less advantageous for all mixtures. The optimum nano silica ratio in concrete is affected by its particle size as well as water to binder ratio. The feasibility of the impact-echo method for identifying the corrosion was evaluated and compared to the corrosion obtained by mass loss method. The results showed that the impact-echo testing can be effectively used to qualitatively detect the damage caused by corrosion in reinforced concrete structures. A significant difference in the dominant frequencies response was observed after exposure to the high and moderate levels of corrosion, whilst no clear trend was observed at the initial stage of corrosion. Artificial neural network models were also developed to predict bond strength for corroded/uncorroded steel bars in concrete using the main influencing parameters (i.e., concrete strength, concrete cover, bar diameter, embedment length and corrosion rate). The developed models were able to predict the bond strength with a high level of accuracy, which was confirmed by conducting a parametric study.Higher Education Institute in the Libyan Government MONE BROS Company in Leeds (UK) for providing recycled aggregates BASF and Akzonobel Companies for providing nano silica NS, Hanson Ltd, UK, for suppling cemen

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively.Comment: 34 Pages,25 Figure

Evaluation of load capacity of concrete railway slab spans with defects

Tese de Doutoramento - Área do Conhecimento em EstruturasThis study presents a complete methodology for the load capacity assessment of existing damaged railway slab spans made of reinforced concrete. The databases of the Polish (PKP) and Portuguese (REFER) railway infrastructure administrations are analysed. Furthermore, a study on the reports of international scientific projects i.e. “Sustainable Bridges” and “SAMCO” have lead to a conclusion that from among existing railway bridge stock, simply supported spans are the most common form of construction. Moreover, the spans carrying single tracks dominate in this group. The statistical information based on these sources has been presented in this study. Based on the Author’s observations, consultations with bridge inspectors as well as the literature study, a uniform and multi-level classification system of bridge defects has been presented. This system reflects all the defects that may occur during a bridges service life. Their influence on the load capacity is emphasized. To evaluate the load capacity of damaged structure information on defect parameters is essential. For this reason the Author presented a survey of testing methods to be applied in bridge condition appraisal. To explain the nature of defects in terms of their causes, the taxonomy of degradation mechanisms, leading to these defects, is presented as well. The defects considered in this study have also been presented in terms of their modelling using of various geometry models. The conception of a numerical defect modelling by means of three parameters (i.e. intensity, location and extent) is included. Conclusions following from the statistical survey on the railway bridge stock have induced the Author to analyse the possibility of application of a simplified geometry model of a span – simply supported beam. In order to compare results of the load capacity assessment several numerical analyses have been performed. The range of application of the simplified span model has been established by means of a 5% threshold of difference between results obtained by the considered models. To perform effective analyses of the load capacity of an existing span, the Author created and presented his own program called “Damage Assessment Graphic Analyser” (DAGA). By means of a built-in graphic editor this program allows visualizations in a three dimensional space of a span with defects in the concrete and reinforcing steel, i.e. losses of material and material parameters modifications. This tool automatically performs the static-strength analysis and results, presented as envelopes of cross-section load capacity (for designed as well as current condition – with defects) and bending moments for various load classes. Using the DAGA program a set of parametric analyses (static-strength) has been carried out. Their results have been collected in a knowledge base to be implemented in an expert tool, called ANAlisys of CONcrete DAmages (ANACONDA) based on the hybrid network technology with analytical and neural components incorporated, designed by the Author. At the end of this Thesis the conclusions and directions of further investigation can be found.A presente tese apresenta uma metodologia avançada para a análise / avaliação da capacidade resistente de pontes ferroviárias com tabuleiro em laje de betão. A análise das bases de dados das administrações ferroviárias polaca (PKP) e portuguesa (REFER), para além do estudo de relatórios recentes de projectos científicos internacionais, tais como exemplo os projectos " Sustainable Bridges" e "SAMCO", permitiram concluir que das pontes ferroviárias existentes a maioria corresponde a soluções simplesmente apoiadas. Entre estas as pontes com via única são claramente o tipo dominante. Neste estudo é apresentada toda a informação estatística dos dados coligidos nessas fontes. Tendo por base as observações do autor, as consultas aos inspectores de pontes, assim como o estudo exaustivo da literatura existente, foi apresentado um sistema de classificação uniforme e multi-nível das anomalias mais comuns em pontes. Este sistema reproduz as anomalias que ocorrem durante o período de vida útil de uma ponte. A influência destas anomalias na capacidade resistente é devidamente abordada. A resposta a este problema tem sido objecto de grande atenção pela comunidade técnica e científica, devido aos desenvolvimentos ocorridos nos métodos experimentais de apoio às inspecções e ao aprofundamento do conhecimento da classificação dos mecanismos de degradação. As anomalias acima mencionadas foram apresentadas, em termos da sua modelação, através de vários modelos geométricos. Para o efeito a modelação numérica de qualquer anomalia é efectuada tendo por base três parâmetros – intensidade, localização e extensão. As conclusões retiradas da análise estatística das pontes ferroviárias existentes induziram o autor a desenvolver um sistema de análise da possibilidade de aplicação de um modelo geométrico, simplificado, para a modelação do efeito das anomalias neste tipo de pontes. O campo de aplicação do modelo simplificado foi estabelecido como válido para as situações correspondentes a 5% de diferença entre os resultados obtidos pelos modelos em questão. Para efectuar as análises da capacidade resistente das pontes em questão, o autor desenvolveu um programa de cálculo automático designado "DAGA – Damage Assessment Graphic Analyser" (DAGA). Através da inserção de um editor gráfico o programa permite a visualização 3D das anomalias existentes no betão e armaduras (perda de materiais e modificação das propriedades dos materiais). Esta ferramenta permite a análise e visualização das envolventes da capacidade resistente para diversos casos de carga e para diversos cenários de anomalias. Mediante a utilização programa DAGA foram efectuados estudos paramétricos (comportamento estático) e os seus resultados foram coligidos numa base de dados a ser posteriormente usada modelos de inteligência artificial. Para o efeito desenvolveu, ainda, um programa de cálculo designado por "ANACONDA – ANAlisys of CON crete DAmages" baseada na tecnologia de redes híbridas com componentes analíticas e neuronais incorporadas. Finalmente, são apresentadas as principais conclusões e a recomendação de futuras linhas de investigação neste campo

Soft computing models for assessing bond performance of reinforcing bars in concrete at high temperatures

The bond between steel and concrete in reinforced concrete structures is a multifaceted and intricate phenomenon that plays a vital role in the design and overall performance of such structures. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. Under elevated temperatures, the bond is more complex under higher temperatures, yet having an accurate estimate is an important factor in design. Therefore, this paper focuses on using data-driven models to explore the performance of the concrete-steel bond under high temperatures using a Gene Expression Programming (GEP) soft computing model. The GEP models are developed to simulate the bond performance in order to understand the effect of high temperatures on the concrete-steel bond. The results were compared to the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) models for different input variables. The new model would help the designers with strength predictions of the bond in fire. The dataset used for the model was obtained from experiments conducted in a laboratory setting that gathered a 316-point database to investigate concrete bond strength at a range of temperatures and with different fibre contents. This study also investigates the impact of the different variables on the equation using sensitivity analysis. the results show that the GEP models are able to predict bond performance with different input variables accurately. This study provides a useful tool for engineers to better understand the concrete-steel bond behaviour under high temperatures and predict concrete-steel bond performance under high temperatures

Fracture characterisation and performance evaluation of corroded RC members by AE-based data analysis

Steel reinforcement corrosion has been regarded as one of the major causes of reinforced concrete (RC) structures failing prematurely, posing a serious structural durability problem worldwide. Detailed assessment of corrosion-induced damage and its effects on RC structures is critical for sustaining structural reliability and safety. This study develops and examines the feasibility of acoustic emission (AE) monitoring and data analysis methodologies to characterise corrosion-induced damage in RC members, followed by an evaluation of the effect of corrosion on load behaviour. Experimental investigations were conducted on a series of specimens of different configurations, namely concrete cubes with steel bars for pull-out tests and RC beams of different dimensions to be subjected to static and cyclic loading regimes. Focusing on developing evaluation methods based on AE monitoring and data analysis, a summary of work completed, and the associated findings are given as follows. Characterisation of the concrete cracking using parametric and waveform analysis was conducted to investigate the effect of corrosion on steel-concrete bond behaviour in the pull-out tests of concrete cubes. It was found that a small amount of corrosion (approximately 6%) could slightly increase the bond strength as a result of the rust expansion and reactionary confinement of concrete. Corrosion was also found to be able to mitigate the damage caused by cyclic loading. AE signal analysis indicates that the concrete cracking mode during the steel-concrete de-bonding process has changed as a result of steel corrosion. Characterisation of load behaviour and failure mode of corroded RC beams was conducted by flexural load tests aided by AE monitoring and digital image correlation (DIC). The DIC strain mapping results and AE signal features revealed that corrosion has an influence on the concrete cracking mechanism of the beam specimens. Corrosion has also altered the failure mode of a shear-critical beam specimen series to flexure owing to the change of steel-concrete bond behaviour. Numerical simulation of AE wave front propagation in RC media and tomographic evaluation of internal damage was implemented on one group of RC beam specimens tested in this study. The numerical model of the specimens was discretised using finite-difference grid meshing, and the different acoustic properties of steel and concrete were defined. On this basis, simulation of AE wave front propagation considering concrete cover cracking and steel rust layer formation was carried out using the fast-marching method. The effect of corrosion-induced damage on the AE rays was studied by examining non-linear ray tracing in the simulation. A tomographic reconstruction approach that solved by the quasi-Newton method provided a potential way to quantitatively evaluate the internal damage of RC beams using AE monitoring data. A novel method was developed for assessing the corrosion level in RC beams using a data-driven approach. Normalization of AE data was applied using principal component analysis to minimise variations in AE signal features caused by differences in the geometrical and material properties of RC beams as well as in the AE monitoring instrumentation setup. The machine learning models, including k-nearest neighbours (KNN) and support vector machines (SVM), were trained using the normalised AE features. The trained KNN models were found effective at predicting the corrosion level in RC beams using the secondary AE signals as input, which could be acquired from the cyclic loading of beams. Key words: Steel Corrosion, Concrete cracking, Steel-Concrete Bond, Reinforced Concrete Beam, Load Behaviour, Acoustic Emission, Digital Image Correlation, Tomographic Reconstruction, Data-driven

Extending BIM for air quality monitoring

As we spend more than 90% of our time inside buildings, indoor environmental quality is a major concern for healthy living. Recent studies show that almost 80% of people in European countries and the United States suffer from SBS (Sick Building Syndrome), which affects physical health, productivity and psychological well-being. In this context, environmental quality monitoring provides stakeholders with crucial information about indoor living conditions, thus facilitating building management along its lifecycle, from design, construction and commissioning to usage, maintenance and end-of-life. However, currently available modelling tools for building management remain limited to static models and lack integration capacities to efficiently exploit environmental quality monitoring data. In order to overcome these limitations, we designed and implemented a generic software architecture that relies on accessible Building Information Model (BIM) attributes to add a dynamic layer that integrates environmental quality data coming from deployed sensors. Merging sensor data with BIM allows creation of a digital twin for the monitored building where live information about environmental quality enables evaluation through numerical simulation. Our solution allows accessing and displaying live sensor data, thus providing advanced functionality to the end-user and other systems in the building. In order to preserve genericity and separation of concerns, our solution stores sensor data in a separate database available through an application programming interface (API), which decouples BIM models from sensor data. Our proof-of-concept experiments were conducted with a cultural heritage building located in Bled, Slovenia. We demonstrated that it is possible to display live information regarding environmental quality (temperature, relative humidity, CO2, particle matter, light) using Revit as an example, thus enabling end-users to follow the conditions of their living environment and take appropriate measures to improve its quality.Pages 244-250

Implementation of Soft Computing Techniques in Forecasting Compressive Strength and Permeability of Pervious Concrete Blended with Ground Granulated Blast-furnace Slag

Urban expansion and infrastructure development have exacerbated environmental issues by creating impermeable layers on the earth's surface, resulting in flash floods and reduced groundwater levels. These problems can be alleviated by using pervious concrete to enhance pavement drainage capacities. However, pervious concrete has limited applications due to its lower strength properties, which are attributed to its mix proportions featuring minimal fine aggregate quantities and an open-graded mix. This study examines the impact of incorporating Ground Granulated Blast-furnace Slag (GGBS) as a supplementary cementitious material in pervious concrete on its strength, drainage capabilities, and water absorption. Further, Artificial Neural Networks (ANN) were used to predict the mechanical and permeability properties of pervious concrete mixes with varying GGBS proportions. The study's results indicate that using GGBS as a 35% partial cement replacement with 10 mm aggregates significantly increases compressive and flexural strength by 28% and 20%, respectively. While permeability values were slightly reduced, they remained within acceptable limits for drainage properties. The developed ANN models outperformed the traditional MLR model, serving as a viable substitute logical tool for forecasting strength as well as permeability. Ultimately, adding GGBS to pervious concrete not only enhances strength but also contributes to environmentally friendly construction practices

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings