Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.Peer ReviewedPostprint (published version

Application of Gene Expression Programming (GEP) for the prediction of compressive strength of geopolymer concrete

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frame

Data-driven optimization tool for the functional, economic, and environmental properties of blended cement concrete using supplementary cementitious materials

The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granulated blast-furnace slag, silica fume, calcined clay and limestone powder, have proven to be the best candidates for sustainable concrete mixes. However, a reliable sustainability measure includes not only the environmental impact, but also the economic and functional ones. Within these five SCMs, their environmental, economic and functional properties are found to be conflicting at times, making a clear judgement on what would be the optimum mix not a straightforward path. A recent framework and tool for concrete sustainability assessment ECO2, sets a reliable methodology for including the functional performance of a concrete mix depending on project-based specifications. Therefore, in this study, a recently published regression model, Pre-bcc was used to predict the functional properties of a wide grid search of potentially suitable blended cement concrete mixes. Hence, an open access novel genetic algorithm tool “Opt-bcc” was developed and used to optimize the sustainability score of these mixes based on a set selection of user-defined project-specific functional criteria. The optimized mixes using the Opt-bcc model for each strength class were compared against the mix design proposed by other optimization models from the literature and were found to be at least 70% cheaper and of 30% less environmental impact.Peer ReviewedPostprint (published version

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

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

Optimal seismic retrofitting of existing RC frames through soft-computing approaches

2016 - 2017Ph.D. Thesis proposes a Soft-Computing approach capable of supporting the engineer judgement in the selection and design of the cheapest solution for seismic retrofitting of existing RC framed structure. Chapter 1 points out the need for strengthening the existing buildings as one of the main way of decreasing economic and life losses as direct consequences of earthquake disasters. Moreover, it proposes a wide, but not-exhaustive, list of the most frequently observed deficiencies contributing to the vulnerability of concrete buildings. Chapter 2 collects the state of practice on seismic analysis methods for the assessment the safety of the existing buildings within the framework of a performancebased design. The most common approaches for modeling the material plasticity in the frame non-linear analysis are also reviewed. Chapter 3 presents a wide state of practice on the retrofitting strategies, intended as preventive measures aimed at mitigating the effect of a future earthquake by a) decreasing the seismic hazard demands; b) improving the dynamic characteristics supplied to the existing building. The chapter presents also a list of retrofitting systems, intended as technical interventions commonly classified into local intervention (also known “member-level” techniques) and global intervention (also called “structure-level” techniques) that might be used in synergistic combination to achieve the adopted strategy. In particular, the available approaches and the common criteria, respectively for selecting an optimum retrofit strategy and an optimal system are discussed. Chapter 4 highlights the usefulness of the Soft-Computing methods as efficient tools for providing “objective” answer in reasonable time for complex situation governed by approximation and imprecision. In particular, Chapter 4 collects the applications found in the scientific literature for Fuzzy Logic, Artificial Neural Network and Evolutionary Computing in the fields of structural and earthquake engineering with a taxonomic classification of the problems in modeling, simulation and optimization. Chapter 5 “translates” the search for the cheapest retrofitting system into a constrained optimization problem. To this end, the chapter includes a formulation of a novel procedure that assembles a numerical model for seismic assessment of framed structures within a Soft-Computing-driven optimization algorithm capable to minimize the objective function defined as the total initial cost of intervention. The main components required to assemble the procedure are described in the chapter: the optimization algorithm (Genetic Algorithm); the simulation framework (OpenSees); and the software environment (Matlab). Chapter 6 describes step-by-step the flow-chart of the proposed procedure and it focuses on the main implementation aspects and working details, ranging from a clever initialization of the population of candidate solutions up to a proposal of tuning procedure for the genetic parameters. Chapter 7 discusses numerical examples, where the Soft-Computing procedure is applied to the model of multi-storey RC frames obtained through simulated design. A total of fifteen “scenarios” are studied in order to assess its “robustness” to changes in input data. Finally, Chapter 8, on the base of the outcomes observed, summarizes the capabilities of the proposed procedure, yet highlighting its “limitations” at the current state of development. Some possible modifications are discussed to enhance its efficiency and completeness. [edited by author]XVI n.s

Developing ECO2: a performance based ecological and economic framework and tool for sustainability assessment of concrete

The use of concrete is associated with immense negative environmental impacts. More than 50 billion tonnes of aggregates are extracted annually for use in concrete, which presents high risks of depleting natural resources. Moreover, concrete has an embodied carbon footprint of 350 kg eq CO2/m3 on average of which 90% is attributable to the production of ordinary Portland cement (OPC). Although this is less than that of steel and most polymers per unit mass, the intensive use of concrete results in an alarming 7% share of the global carbon emissions. Therefore, increasing interest is being directed towards producing sustainable concrete. Conducting a Life cycle assessment (LCA) is a widely accepted tool to assess and compare the acclaimed environmental gains of these sustainable concrete types, while calculating the base line cost of each of these mixes could suffice for economic comparisons. However, sustainability is a multifaceted concept and in order to validate the sustainable of a concrete mix, multi criteria sustainability frameworks are needed. The critical examination of the only two frameworks found in the literature that fits this description, MARS-SC and CONCRETop, showed the need to develop a new one that covers their gaps, which inspired the main contribution in this PhD project. A novel ECOnomic and ECOlogical assessment framework for concrete (hence the name ECO2 which also refers to the symbolic carbon dioxide formula) was created with the following distinguishing features: 1. The scope specified for the LCA study is selected as Cradle-to-Grave in order to account for the whole life cycle of concrete. Therefore, the LCA inventory data, for which sitespecific primary data is prioritized, would include upstream data such as the impact allocation from previous processes from which the raw materials originated and downstream data such as the demolition and disposal impact of concrete. 2. The ECO2 framework considers the amount of carbon sequestration, which is the term used to describe how much carbon dioxide is absorbed by concrete from the environment. The accurate calculation of the carbon footprint of a concrete mix is vital for its absolute environmental impact assessment, but would soon in the near future also affect its economic impact when carbon taxation becomes a normal practice. Aside from filling the technical gaps of the sustainability assessment method, the main contribution the ECO2 framework brings is a shift in the philosophy related to the inclusion of the concrete performance in the process. In both reviewed frameworks (MARS-SC and CONCRETop), concrete performance is assessed as a separate pillar of sustainability perpetuating that the higher performance is rewarded with a higher sustainability index value. Instead, the ECO2 framework brings forward a two layered performance based methodology that promotes a value of resource efficiency. First, the user sets a minimum requirement for the workability and strength depending on the project specifications. The second layer is to correlate the expected service life of each qualifying concrete mix to the required service life of the concrete application within the project through a factor N. This factor, for which the minimum value is 1, is then multiplied by the functional unit used for the LCA to ensure that the economic and ecological assessment are not only accurate but also truly reflective of sustainability. An MS excel tool was also developed to self-validate the ECO2 framework in what could be labelled as a methodical contribution. Finally, three case studies were conducted using the newly developed ECO2 framework as follows: 1. The first case study was experimental using electric arc furnace slag as a precursor for alkali activated concrete and comparing its ECO2 sustainability index to a basic alkali activated concrete mix based on fly ash as a precursor. The case study showed that the deterioration in the mechanical properties of the novel alkali activated slag concrete largely overshadow the ecological and economic merits of recycling it. 2. The second case study was analytical using a database of more than 2500 data points to predict and hence optimize the functional, environmental and economic performance of blended cement concrete using the ECO2 framework. The mixes included varying combinations of five different types of SCMs based on plain and reinforced concrete scenarios of different strength and service life requirements. 3. The final case study was prepared to investigate an issue facing the UK Green concrete market which is the need to shut down all coal operated electrical power plants by 2022 and the subsequent absence of fly ash. The case study used the ECO2 framework to compare between importing fly ash from China, Germany and recycling locally existing stockpiled fly ash in the UK. The vital parameter in the comparison was the environmental and economic impact resulting from the transportation of fly ash from its source to the location of the concrete batch plant in the UK

Computational Modelling of Concrete and Concrete Structures

Computational Modelling of Concrete and Concrete Structures contains the contributions to the EURO-C 2022 conference (Vienna, Austria, 23-26 May 2022). The papers review and discuss research advancements and assess the applicability and robustness of methods and models for the analysis and design of concrete, fibre-reinforced and prestressed concrete structures, as well as masonry structures. Recent developments include methods of machine learning, novel discretisation methods, probabilistic models, and consideration of a growing number of micro-structural aspects in multi-scale and multi-physics settings. In addition, trends towards the material scale with new fibres and 3D printable concretes, and life-cycle oriented models for ageing and durability of existing and new concrete infrastructure are clearly visible. Overall computational robustness of numerical predictions and mathematical rigour have further increased, accompanied by careful model validation based on respective experimental programmes. The book will serve as an important reference for both academics and professionals, stimulating new research directions in the field of computational modelling of concrete and its application to the analysis of concrete structures. EURO-C 2022 is the eighth edition of the EURO-C conference series after Innsbruck 1994, Bad Gastein 1998, St. Johann im Pongau 2003, Mayrhofen 2006, Schladming 2010, St. Anton am Arlberg 2014, and Bad Hofgastein 2018. The overarching focus of the conferences is on computational methods and numerical models for the analysis of concrete and concrete structures

Prediction of Time-Dependent Deflection of High Strength Concrete Panels

This work presents a model for predicting analytically the time dependent deflection of high strength concrete HSC slabs. This model considers the factors that are significantly influence the long-term deflection of concrete slabs. Realising the effect of time on slab flexural rigidity, the proposed method follow the method of conducting short-term deflection of slabs.The analytical deflection based on the proposed method are compared with the experimental work conducted by the authors in 2005 (1) and also with several field measured deflections

RESIDENTIAL BUILDING DEVELOPMENT PROCESS IN KURDISTAN REGION GOVERNMENT

Nowadays, Residential buildings have become the most important part of real-estate markets in (KRG). The layout of housing in Kurdistan has transformed the face of major cities across the Region. Rapid changes since 2003, have witnessed copious architectural structures and large housing projects that have reshaped the landscape of its cities. The aim of this study is to study the housing developing policy in KRG. The objectives of the study are to evaluate the KRG's housing development policy and to investigate the types of house and the price range preferred by the potential buyer. The study focus on private residential building development projects and it is carried out by questionnaires and interviews. The respondents are the house buyers and the developers. A total of 100 questionnaires were distributed to the respondents and 78 questionnaires were returned duly answered. The data collected is analyzed using the SPSS (Statistical Package for the Social Sciences) and Average Index. The results of research indicated that the KRG’s housing development policy covers the ownership of the project land, full repatriation of project investment and profits allowed, import of spare parts tax exempt up to 15% of project cost and the employment of foreign workers allowed. Moreover, the types of house preferred by the house buyers are of double storey type and to be of corner lot. The price range preferred by the potential buyers are between (40,000 to 100,000) USD