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.

This study proposes and demonstrates a smart monitoring system that uses transmission lines embedded in a reinforced concrete structure to detect the presence of defects through changes in the electromagnetic waves generated and measured by a time-domain reflectometer. Laboratory experiments were first conducted to identify the presence of voids in steel-concrete composite columns. The results indicated that voids in the concrete caused a positive signal reflection, and the amplitude of this signal decreased as the water content of the soil in the void increased. Multiple voids resulted in a decrease in the amplitude of the signal reflected at each void, effectively identifying their presence despite amplitude reduction. Furthermore, the electromagnetic wave velocity increased when voids were present, decreased as the water content of the soil in the voids increased, and increased with the water-cement ratio and curing time. Field experiments were then conducted using bored piles with on-center (sound) and off-center (defective) steel-reinforcement cage alignments. The results indicated that the signal amplitude in the defective pile section, where the off-center cage was poorly covered with concrete, was greater than that in the pile sections where the cage was completely covered with concrete. The crosshole sonic logging results for the same defective bored pile failed to identify an off-center cage alignment defect. Therefore, this study demonstrates that electromagnetic waves can be a useful tool for monitoring the health and integrity of reinforced concrete structures.

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.

Calcium leaching is one of the main deterioration factors in concrete structures contact with water, such as dams, water treatment structures, and radioactive waste structures. It causes a porous microstructure and may be coupled with various harmful factors resulting in mechanical degradation of concrete. Several numerical modeling studies focused on the calcium leaching depth prediction. However, these required a lot of cost and time for many experiments and analyses. This study presents an artificial neural network (ANN) approach to predict the leaching depth quickly and accurately. Totally 132 experimental data are collected for model training and validation. An optimal ANN model was proposed by ANN topology. Results indicate that the model can be applied to estimate the calcium leaching depth, showing the determination coefficient of 0.91. It might be used as a simulation tool for engineering problems focused on durability.

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.

Shear wall is commonly used as a lateral force resisting system of concrete mid-rise and high-rise buildings, but it brings challenges in providing relatively large space throughout the building height. For this reason, the structure system where the upper structure with bearing, non-bearing and/or shear walls that sits on top of a transfer plate system supported by widely spaced columns at the lower stories is preferred in some regions, particularly in low to moderate seismic regions in Asia. A thick reinforced concrete (RC) plate has often been used as a transfer system, along with RC transfer girders; however, the RC plate becomes very thick for tall buildings. Applying the post-tensioning (PT) technique to RC plates can effectively reduce the thickness and reinforcement as an economical design method. Currently, a simplified model is used for numerical modeling of PT transfer plate, which does not consider the interaction of the plate and the upper structure. To observe the actual behavior of PT transfer plate under seismic loads, it is necessary to model whole parts of the structure and tendons to precisely include the interaction and the secondary effect of PT tendons in the results. This research evaluated the seismic behavior of shear wall-type residential buildings with PT transfer plates for the condition that PT tendons are included or excluded in the modeling. Three-dimensional finite element models were developed, which includes prestressing tendon elements, and response spectrum analyses were carried out to evaluate seismic forces. Two buildings with flat-shape and L-shape plans were considered, and design forces of shear walls and transfer columns for a system with and without PT tendons were compared. The results showed that, in some cases, excluding PT tendons from the model leads to an unrealistic estimation of the demands for shear walls sit on transfer plate and transfer columns due to excluding the secondary effect of PT tendons. Based on the results, generally, the secondary effect reduces shear force demand and axial-flexural demands of transfer columns but increases the shear force demand of shear walls. The results of this study suggested that, in addition to the effect of PT on the resistance of transfer plate, it is necessary to include PT tendons in the modeling to consider its effect on force demand.

This paper describes an in-depth analysis on flexural strength of a girder-deck system experiencing a strand debonding damage with various strengthening systems, based on finite element software ABAQUS. A detailed finite element analysis (FEA) model was developed and verified against the relevant experimental data performed by other researchers. The proposed analytical model showed a good agreement with experimental data. Based on the verified FE model, over a hundred girder-deck systems were investigated with the consideration of following variables: 1) debonding level, 2) span-to-depth ratio (L/d), 3) strengthening type, 4) strengthening material thickness. Based on the data above, a new detailed analytical model was developed and proposed for estimating residual flexural strength of the strand-debonding damaged girder-deck system with strengthening systems. It was demonstrated that both finite element model and analysis model could be used to predict flexural behaviors for debonding damaged prestressed girder-deck systems. Since the strands are debonding from surrounding concrete over a certain zone over the length of the beam, the increase of strain in strands can be linked with a ratio ψ, which is L_p/c. The analytical model was proposed and developed regarding the ratio ψ. By conducting procedure of calculating ψ, the ψ value varies from 9.3 to 70.1. Multiple nonlinear regression analysis was performed in Software IBM SPSS Statistics 27.0.1 to derive equation of ψ. ψ equation was curved to be an exponential function, and the independent variable (X) is a linear function in terms of three variables of debonding level (λ), span length (L), and amount of strengthening material (A_s). The coefficient of determinate (R²) for curve fitting in nonlinear regression analysis is 0.8768. The developed analytical model was compared to the ultimate capacities computed by FEA model.

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.