• Computers and Concrete
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• v.31 no.1
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• pp.53-69
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• 2023

The rotational stiffness of a semi-rigid beam-to-column connection plays an important role in the reduction of the second-order effects in the precast concrete skeletal frames. The aim of this study is to present a detailed nonlinear finite element study to reproduce the experimental response of a semi-rigid precast beam-to-column connection composed by corbel, dowel bar and continuity tie bars available in the literature. A parametric study was carried using four arrangements of the reinforcing tie bars in the connection, including high ratio of the continuity tie bars passing around the column in the cast-in-place concrete. The results from the parametric study were compared to analytical equations proposed to evaluate the secant rotational stiffness of beam-to-column connections. The good agreement with the experimental results was obtained, demonstrating that the finite element model can accurately predict the structural behaviour of the beam-to-column connection despite its complex geometric configuration. The secant rotational stiffness of the connection was good evaluated by the analytical model available in the literature for ratio of the continuity tie bars of up to 0.69%. Precast beam-to-column connection with a ratio of the continuity tie bars higher than 1.4% had the secant stiffness overestimated. Therefore, an adjustment coefficient for the effective depth of the crack at the end of the beam was proposed for the analytical model, which is a function of the ratio of the continuity tie bars.

• Computers and Concrete
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• v.31 no.4
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• pp.327-335
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• 2023

The most popular building material, concrete, is intrinsically linked to the advancement of humanity. Due to the ever-increasing complexity of cementitious systems, concrete formulation for desired qualities remains a difficult undertaking despite conceptual and methodological advancement in the field of concrete science. Recognising the significant pollution caused by the traditional cement industry, construction of civil engineering structures has been carried out successfully using Geopolymer Concrete (GPC), also known as High Performance Concrete (HPC). These are concretes formed by the reaction of inorganic materials with a high content of Silicon and Aluminium (Pozzolans) with alkalis to achieve cementitious properties. These supplementary cementitious materials include Ground Granulated Blast Furnace Slag (GGBFS), a waste material generated in the steel manufacturing industry; Fly Ash, which is a fine waste product produced by coal-fired power stations and Silica Fume, a by-product of producing silicon metal or ferrosilicon alloys. This result demonstrated that GPC/HPC can be utilised as a substitute for traditional Portland cement-based concrete, resulting in improvements in concrete properties in addition to environmental and economic benefits. This study explores utilising experimental data to train artificial neural networks, which are then used to determine the effect of supplementary cementitious material replacement, namely fly ash, Ground Granulated Blast Furnace Slag (GGBFS) and silica fume, on the compressive strength, tensile strength, and modulus of elasticity of concrete and to predict these values accordingly.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Computers and Concrete
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• v.32 no.3
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• pp.339-349
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• 2023

Fast-built construction is a key feature for successful applications of precast concrete (PC) moment frame system in recent construction practices. To this end, by introducing some unique splicing details in precast connections, especially between PC columns including panel zones, use of temporary supports and bracings can be minimized based on their self-sustaining nature. In addition, precast wide beams are commonly adopted for better economic feasibility. In this study, three self-sustaining precast concrete (PC) wide beam-column connection specimens were fabricated and tested under reversed cyclic loadings, and their seismic performances were quantitatively evaluated in terms of strength, ductility, failure modes, energy dissipation and stiffness degradation. Test results were compared with ASCE 41-17 nonlinear modeling curves and its corresponding acceptance criteria. On this basis, an improved macro modeling method was explored for a more accurate simulation. It appeared that all the test specimens fully satisfy the acceptance criteria, but the implicit joint model recommended in ASCE 41-17 tends to underestimate the joint shear stiffness of PC wide beam-column connection. While, the explicit joint model along with concentrated plastic hinge modeling technique is able to present better accuracy in simulating the cyclic responses of PC wide beam-column connections.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Computers and Concrete
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• v.32 no.3
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• Computers and Concrete
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• v.31 no.4
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• pp.307-314
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• 2023

In this paper we back-analyze a failure event of a 9 m high concrete cantilever wall subjected to earth loading. Granular soil was deposited into the space between the wall and a nearby rock slope. The wall segments were not designed to carry lateral earth loading and collapsed due to excessive bending. As many geotechnical programs rely on the Mohr-Coulomb (MC) criterion for elastoplastic analysis, it is useful to apply this failure criterion to the concrete material. Accordingly, the back-analysis is aimed to search for the suitable MC parameters of the concrete. For this study, we propose a methodology for accelerating the back-analysis task by automating the numerical modeling procedure and applying a machine-learning (ML) analysis on FE model results. Through this analysis it is found that the residual cohesion and friction angle have a highly significant impact on model results. Compared to traditional back-analysis studies where good agreement between model and reality are deemed successful based on a limited number of models, the current ML analysis demonstrate that a range of possible combinations of parameters can yield similar results. The proposed methodology can be modified for similar calibration and back-analysis tasks.

• Computers and Concrete
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• v.31 no.4
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• pp.359-370
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• 2023

Slime is produced by excavation during the installation of embedded piles, and it tends to mix with the cement paste injected into the pile shafts. The objective of this study is to investigate the strength and stiffness characteristics of cement pasteslime mixtures. Mixtures with different slime ratios are prepared and cured for 28 days. Uniaxial compression tests and elastic wave measurements are conducted to obtain the static and dynamic properties, respectively. The uniaxial compressive strengths and static elastic moduli of the mixtures are evaluated according to the curing period, slime ratio, and water-cement ratio. In addition, dynamic properties, e.g., the constrained, shear, and elastic moduli, are estimated from the compressional and shear wave velocities. The experimental results show that the static and dynamic properties increase under an increase in the curing period but decrease under an increase in the slime and water-cement ratios. The cement paste-slime mixtures show several exponential relationships between their static and dynamic properties, depending on the slime ratio. The bearing mechanisms of embedded piles can be better understood by examining the strength and stiffness characteristics of cement paste-slime mixtures.

• Computers and Concrete
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• v.31 no.5
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• pp.443-455
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• 2023

This is a research analyses on the bearing capacity at a pile tip embedded in rock. The aim is to propose a shape coefficient for an analytical solution and to investigate the influence of the plastic flow law on the problem. For this purpose, the finite difference method is used to analyze the bearing capacity of various types and states of rock masses, assuming the Hoek & Brown failure criterion, by considering both plane strain and an axisymmetric model. Different geometrical configurations were adopted for this analysis. First, the axisymmetric numerical results were compared with those obtained from the plane strain analytical solution. Then the pile shape influence on the bearing capacity was studied. A shape factor is now proposed. Furthermore, an evaluation was done on the influence of the plastic flow law on the pile tip bearing capacity. Associative flow and non-associative flow with null dilatancy were considered, resulting in a proposed correlation. A total of 324 cases were simulated, performing a sensitivity analysis on the results and using the graphic output of vertical displacement and maximum principal stress to understand how the failure mechanism occurs in the numerical model.