Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.
In recent few years, significant improvement has been made in developing largescale 3D printers to accommodate the need of industrial-scale 3D printing. It is of great feasibility to construct structural components and buildings by means of 3D concrete printing. The major issues of this innovative technique focus on the preparation and optimization of concrete materials which possess favourable printable properties as well as the measurement and evaluation methods of their workability. This paper firstly introduces three largescale 3D printing systems that have been successfully applied in construction industry. It then summarizes the commonly used raw materials in concrete manufacturing. Critical factors that should be particularly controlled in material preparation are specified. Easy-extrusive, easy-flowing, well-buildable, proper setting time and low shrinkage are significant for concrete mixture to meet the critical requirements of a freeform construction process. Thereafter, measuring methods that can be employed to assess the fresh and hardened properties of concrete at early stages are suggested. Finally, a few of evaluation methods are presented which may offer certain assistance for optimizing material preparation. The objective of this work is to review current design methodologies and experimental measurement and evaluation methods for 3D printable concrete materials and promote its responsible use with largescale 3D printing technology.
Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R2) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.
On June 24, 2021, a 40-year-old reinforced concrete flat plate structure building in Miami suffered a sudden partial collapse. This study analyzed the overall performance and key components of the collapsed building based on the building design codes (ACI-318 and GB 50010). Punching shear and post-punching performances of typical slab-column joints are also studied through the refined finite element analysis. The collapse process was simulated and visualized using a physics engine. By way of these analyses, weak design points of the collapsed building are highlighted. The differences between the reinforcement detailing of the collapsed building and the requirements of the current Chinese code are discussed, together with a comparison of the punching shear and post-punching performances. The simulated collapse procedure and debris distribution are compared with the actual collapse scenes.
Fragility curves are commonly used in civil engineering to assess the vulnerability of structures to earthquakes. The probability of failure associated with a prescribed criterion (e.g., the maximal inter-storey drift of a building exceeding a certain threshold) is represented as a function of the intensity of the earthquake ground motion (e.g., peak ground acceleration or spectral acceleration). The classical approach relies on assuming a lognormal shape of the fragility curves; it is thus parametric. In this paper, we introduce two non-parametric approaches to establish the fragility curves without employing the above assumption, namely binned Monte Carlo simulation and kernel density estimation. As an illustration, we compute the fragility curves for a three-storey steel frame using a large number of synthetic ground motions. The curves obtained with the non-parametric approaches are compared with respective curves based on the lognormal assumption. A similar comparison is presented for a case when a limited number of recorded ground motions is available. It is found that the accuracy of the lognormal curves depends on the ground motion intensity measure, the failure criterion and most importantly, on the employed method for estimating the parameters of the lognormal shape.
Using of rubber asphalt can both promote the recycling of waste tires and improve the performance of asphalt pavement. However, the segregation of rubber asphalt caused by the poor storage stability always appears during its application. Storage stability of asphalt and rubber is related to the compatibility and also influenced by rubber content. In this study, molecular models of different rubbers and chemical fractions of asphalt were built to perform the molecular dynamics simulation. The solubility parameter and binding energy between rubber and asphalt were obtained to evaluate the compatibility between rubber and asphalt as well as the influence of rubber content on compatibility. Results show that all three kinds of rubber are commendably compatible with asphalt, where the compatibility between asphalt and cis-polybutadiene rubber (BR) is the best, followed by styrene-butadiene rubber (SBR), and natural rubber (NR) is the worst. The optimum rubber contents for BR asphalt, SBR asphalt, and NR asphalt were determined as 15%, 15%, and 20%, respectively. In addition, the upper limits of rubber contents were found as between 25% and 30%, between 20% and 25%, and between 25% and 30%, respectively.
The objectives of this study are to review and evaluate the developments and applications of pultruded fiber-reinforced polymer composites in civil and structural engineering and review advances in research and developments. Several case applications are reviewed. The paper presents a state-of-the-art review of fundamental research on the behavior of pultruded fiber reinforced polymer closed sections and highlights gaps in knowledge and areas of potential further research.
With increasing environmental pressure to reduce solid waste and to recycle as much as possible, the concrete industry has adopted a number of methods to achieve this goal by replacement of waste glass with concrete composition materials. Due to differences in mixture design, placement and consolidation techniques, the strength and durability of Self Compacting Concrete (SCC) may be different than those of conventional concrete. Therefore, replacement of waste glass with fine aggregate in SCC should deeply be investigated compared to conventional concretes. The aim of the present study is to investigate the effect of glass replacement with fine aggregate on the SCC properties. In present study, fine aggregate has been replaced with waste glass in six different weight ratios ranging from 0% to 50%. Fresh results indicate that the flow-ability characteristics have been increased as the waste glass incorporated to paste volume. Nevertheless, compressive, flexural and splitting strengths of concrete containing waste glass have been shown to decrease when the content of waste glass is increased. The strength reduction of concrete in different glass replacement ratios is not remarkable, thus it can be produced SCC with waste glass as fine aggregate in a standard manner.
A brief overview of vortex-induced vibration (VIV) of circular cylinders is first given as most of VIV studies have been focused on this particular bluff cross-section. A critical literature review of VIV of bridge decks that highlights physical mechanisms central to VIV from a renewed perspective is provided. The discussion focuses on VIV of bridge decks from wind-tunnel experiments, full-scale observations, semi-empirical models and computational fluids dynamics (CFD) perspectives. Finally, a recently developed reduced order model (ROM) based on truncated Volterra series is introduced to model VIV of long-span bridges. This model captures successfully salient features of VIV at “lock-in” and unlike most phenomenological models offers physical significance of the model kernels.
This paper presents a comprehensive review of modeling of alkali-silica reaction (ASR) in concrete. Such modeling is essential for investigating the chemical expansion mechanism and the subsequent influence on the mechanical aspects of the material. The concept of ASR and the mechanism of expansion are first outlined, and the state-of-the-art of modeling for ASR, the focus of the paper, is then presented in detail. The modeling includes theoretical approaches, meso- and macroscopic models for ASR analysis. The theoretical approaches dealt with the chemical reaction mechanism and were used for predicting pessimum size of aggregate. Mesoscopic models have attempted to explain the mechanism of mechanical deterioration of ASR-affected concrete at material scale. The macroscopic models, chemo-mechanical coupling models, have been generally developed by combining the chemical reaction kinetics with linear or nonlinear mechanical constitutive, and were applied to reproduce and predict the long-term behavior of structures suffering from ASR. Finally, a conclusion and discussion of the modeling are given.
Super-long span bridges demand high design requirements and involve many difficulties when constructed, which is an important indicator to reflect the bridge technical level of a country. Over the past three decades, a large percentage of the new long-span bridges around the world were built in China, and thus, abundant technological innovations and experience have been accumulated during the design and construction. This paper aims to review and summarize the design and construction practices of the superstructure, the substructure, and the steel deck paving of the long-span bridges during the past decades as well as the current operation status of the existing long-span bridges in China. A future perspective was given on the developing trend of high-speed railway bridge, bridge over deep-sea, health monitoring and maintenance, intellectualization, standard system, and information technology, which is expected to guide the development direction for the construction of future super long-span bridges and promote China to become a strong bridge construction country.
We propose a method to estimate the natural frequencies of the multi-walled carbon nanotubes (MWCNTs) embedded in an elastic medium. Each of the nested tubes is treated as an individual bar interacting with the adjacent nanotubes through the inter-tube Van der Waals forces. The effect of the elastic medium is introduced through an elastic model. The mathematical model is finally reduced to an eigen value problem and the eigen value problem is solved to arrive at the inter-tube resonances of the MWCNTs. Variation of the natural frequencies with different parameters are studied. The estimated results from the present method are compared with the literature and results are observed to be in close agreement.
This paper presents an efficient hybrid control approach through combining the idea of proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) control algorithm. The proposed LQR-PID controller, while having the advantage of the classical PID controller, is easy to implement in seismic-excited structures. Using an optimization procedure based on a cuckoo search (CS) algorithm, the LQR-PID controller is designed for a seismic- excited structure equipped with an active tuned mass damper (ATMD). Considering four earthquakes, the performance of the proposed LQR-PID controller is evaluated. Then, the results are compared with those given by a LQR controller. The simulation results indicate that the LQR-PID performs better than the LQR controller in reduction of seismic responses of the structure in the terms of displacement and acceleration of stories of the structure.
This paper presents the effect on compressive strength and self-healing capability of bacterial concrete with the addition of calcium lactate. Compared to normal concrete, bacterial concrete possesses higher durability and engineering concrete properties. The production of calcium carbonate in bacterial concrete is limited to the calcium content in cement. Hence calcium lactate is externally added to be an additional source of calcium in the concrete. The influence of this addition on compressive strength, self-healing capability of cracks is highlighted in this study. The bacterium used in the study is bacillus subtilis and was added to both spore powder form and culture form to the concrete. Bacillus subtilis spore powder of 2 million cfu/g concentration with 0.5% cement was mixed to concrete. Calcium lactates with concentrations of 0.5%, 1.0%, 1.5%, 2.0%, and 2.5% of cement, was added to the concrete mixes to test the effect on properties of concrete. In other samples, cultured bacillus subtilis with a concentration of 1×105 cells/mL was mixed with concrete, to study the effect of bacteria in the cultured form on the properties of concrete. Cubes of 100 mm×100 mm×100 mm were used for the study. These cubes were tested after a curing period of 7, 14, and 28 d. A maximum of 12% increase in compressive strength was observed with the addition of 0.5% of calcium lactate in concrete. Scanning electron microscope and energy dispersive X-ray spectroscopy examination showed the formation of ettringite in pores; calcium silicate hydrates and calcite which made the concrete denser. A statistical technique was applied to analyze the experimental data of the compressive strengths of cementations materials. Response surface methodology was adopted for optimizing the experimental data. The regression equation was yielded by the application of response surface methodology relating response variables to input parameters. This method aids in predicting the experimental results accurately with an acceptable range of error. Findings of this investigation indicated the influence of added calcium lactate in bio-concrete which is quite impressive for improving the compressive strength and self-healing properties of concrete.
Reinforced concretes (RC) have been widely used in constructions. In construction, one of the critical elements carrying a high percentage of the weight is columns which were not used to design to absorb large dynamic load like surface bursts. This study focuses on investigating blast load parameters to design of RC columns to withstand blast detonation. The numerical model is based on finite element analysis using LS-DYNA. Numerical results are validated against blast field tests available in the literature. Couples of simulations are performed with changing blast parameters to study effects of various scaled distances on the nonlinear behavior of RC columns. According to simulation results, the scaled distance has a substantial influence on the blast response of RC columns. With lower scaled distance, higher peak pressure and larger pressure impulse are applied on the RC column. Eventually, keeping the scaled distance unchanged, increasing the charge weight or shorter standoff distance cause more damage to the RC column. Intensive studies are carried out to investigate the effects of scaled distance and charge weight on the damage degree and residual axial load carrying capacity of RC columns with various column width, longitudinal reinforcement ratio and concrete strength. Results of this research will be used to assessment the effect of an explosion on the dynamic behavior of RC columns.
In the recent years, the phase field method for simulating fracture problems has received considerable attention. This is due to the salient features of the method: 1) it can be incorporated into any conventional finite element software; 2) has a scalar damage variable is used to represent the discontinuous surface implicitly and 3) the crack initiation and subsequent propagation and branching are treated with less complexity. Within this framework, the linear momentum equations are coupled with the diffusion type equation, which describes the evolution of the damage variable. The coupled nonlinear system of partial differential equations are solved in a ‘staggered’ approach. The present work discusses the implementation of the phase field method for brittle fracture within the open-source finite element software, FEniCS. The FEniCS provides a framework for the automated solutions of the partial differential equations. The details of the implementation which forms the core of the analysis are presented. The implementation is validated by solving a few benchmark problems and comparing the results with the open literature.
During the last decade, numerous high-quality two-dimensional (2D) materials with semiconducting electronic character have been synthesized. Recent experimental study (Sci. Adv. 2017;3: e1700481) nevertheless confirmed that 2D ZrSe2 and HfSe2 are among the best candidates to replace the silicon in nanoelectronics owing to their moderate band-gap. We accordingly conducted first-principles calculations to explore the mechanical and electronic responses of not only ZrSe2 and HfSe2, but also ZrS2 and HfS2 in their single-layer and free-standing form. We particularly studied the possibility of engineering of the electronic properties of these attractive 2D materials using the biaxial or uniaxial tensile loadings. The comprehensive insight provided concerning the intrinsic properties of HfS2, HfSe2, ZrS2, and ZrSe2 can be useful for their future applications in nanodevices.
In the framework of finite element meshes, a novel continuous/discontinuous deformation analysis (CDDA) method is proposed in this paper for modeling of crack problems. In the present CDDA, simple polynomial interpolations are defined at the deformable block elements, and a link element is employed to connect the adjacent block elements. The CDDA is particularly suitable for modeling the fracture propagation because the switch from continuous deformation analysis to discontinuous deformation analysis is natural and convenient without additional procedures. The SIFs (stress intensity factors) for various types of cracks, such as kinked cracks or curved cracks, can be easily computed in the CDDA by using the virtual crack extension technique (VCET). Both the formulation and implementation of the VCET in CDDA are simple and straightforward. Numerical examples indicate that the present CDDA can obtain high accuracy in SIF results with simple polynomial interpolations and insensitive to mesh sizes, and can automatically simulate the crack propagation without degrading accuracy.
Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.
In the present study, the free vibration of laminated functionally graded carbon nanotube reinforced composite beams is analyzed. The laminated beam is made of perfectly bonded carbon nanotubes reinforced composite (CNTRC) layers. In each layer, single-walled carbon nanotubes are assumed to be uniformly distributed (UD) or functionally graded (FG) distributed along the thickness direction. Effective material properties of the two-phase composites, a mixture of carbon nanotubes (CNTs) and an isotropic polymer, are calculated using the extended rule of mixture. The first-order shear deformation theory is used to formulate a governing equation for predicting free vibration of laminated functionally graded carbon nanotubes reinforced composite (FG-CNTRC) beams. The governing equation is solved by the finite element method with various boundary conditions. Several numerical tests are performed to investigate the influence of the CNTs volume fractions, CNTs distributions, CNTs orientation angles, boundary conditions, length-to-thickness ratios and the numbers of layers on the frequencies of the laminated FG-CNTRC beams. Moreover, a laminated composite beam combined by various distribution types of CNTs is also studied.
This paper presents the findings of an experimental program seeking to understand the effect of mineral admixtures on fresh and hardened properties of sustainable self-consolidating concrete (SCC) mixes where up to 80% of Portland cement was replaced with fly ash, silica fume, or ground granulated blast furnace slag. Compressive strength of SCC mixes was measured after 3, 7, and 28 days of moist curing. It was concluded in this study that increasing the dosage of fly ash increases concrete flow but also decreases segregation resistance. In addition, for the water-to-cement ratio of 0.36 used in this study, it was observed that the compressive strength decreases compared to control mix after 28 days of curing when cement was partially replaced by 10%, 30%, and 40% of fly ash. However, a fly ash replacement ratio of 20% increased the compressive strength by a small margin compared to the control mix. Replacing cement with silica fume at 5%, 10%, 15%, and 20% was found to increase compressive strength of SCC mixes compared to the control mix. However, the highest 28 day compressive strength of 95.3 MPa occurred with SCC mixes in which 15% of the cement was replaced with silica fume.
Fibers obtained from different parts of the oil palm tree (Elaeis guineensis) have been under investigation for possible use in construction. Studies have been carried out investigating the engineering properties and possible applications of these fibers. However, the experimental methods employed and the values of mechanical and physical properties recorded by various authors are inconsistent. It has therefore become necessary to organize information which would be useful in the design of oil palm fiber cement composites and help researchers and engineers make informed decisions in further research and application. This review provides information about fibers from different parts of the oil palm, their properties, enhancement techniques, current and potential application in cement composites.
The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.
In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.
One of the strategic materials used in earth-fill embankment dams and in modifying and preventing groundwater flow is plastic concrete (PlC). PlC is comprised of aggregates, water, cement, and bentonite. Natural zeolite (NZ) is a relatively abundant mineral resource and in this research, the microstructure, unconfined strength, triaxial behavior, and permeability of PlC made with 0%, 10%, 15%, 20%, and 25% replacement of cement by NZ were studied. Specimens of PIC-NZ were subjected to confined conditions and three different confining pressures of 200, 350, and 500 kPa were used to investigate their mechanical behavior and permeability. To study the effect of sulfate ions on the properties of PlC-NZ specimens, the specimens were cured in one of two different environments: normal condition and in the presence of sulfate ions. Results showed that increasing the zeolite content decreases the unconfined strength, elastic modulus, and peak strength of PlC-NZ specimens at the early ages of curing. However, at the later ages, increasing the zeolite content increases unconfined strength as well as the peak strength and elastic modulus. Specimens cured in the presence of sulfate ions indicated lower permeability, higher unconfined strength, elastic modulus, and peak strength due to having lower porosity.
Bridge girders exposed to aggressive environmental conditions are subject to time-variant changes in resistance. There is therefore a need for evaluation procedures that produce accurate predictions of the load-carrying capacity and reliability of bridge structures to allow rational decisions to be made about repair, rehabilitation and expected life-cycle costs. This study deals with the stability of damaged steel I-beams with web opening subjected to bending loads. A three-dimensional (3D) finite element (FE) model using ABAQUS for the elastic flexural torsional analysis of I-beams has been used to assess the effect of web opening on the lateral buckling moment capacity. Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting the lateral-torsional buckling moment capacity of deteriorated steel I-beams with different sizes of rectangular web opening using obtained FE results. It is found out that the proposed formulae can accurately predict residual lateral buckling capacities of doubly-symmetric steel I-beams with rectangular web opening. Hence, the results of this study can be used for better prediction of buckling life of web opening of steel beams by practice engineers.
Landslide is a common geological hazard in reservoir areas and may cause great damage to local residents’ life and property. It is widely accepted that rainfall and periodic variation of water level are the two main factors triggering reservoir landslides. In this study, the Bazimen landslide located in the Three Gorges Reservoir (TGR) was back-analyzed as a case study. Based on the statistical features of the last 3-year monitored data and field instrumentations, the landslide susceptibility in an annual cycle and four representative periods was investigated via the deterministic and probabilistic analysis, respectively. The results indicate that the fluctuation of the reservoir water level plays a pivotal role in inducing slope failures, for the minimum stability coefficient occurs at the rapid decline period of water level. The probabilistic analysis results reveal that the initial sliding surface is the most important area influencing the occurrence of landslide, compared with other parts in the landslide. The seepage calculations from probabilistic analysis imply that rainfall is a relatively inferior factor affecting slope stability. This study aims to provide preliminary guidance on risk management and early warning in the TGR area.
Increasing the bending and shear capacities of reinforced concrete members is an interesting issue in structural engineering. In recent years, many studies have been carried out to improve capacities of reinforced concrete members such as using post and pre-tensioning, Fiber Reinforced Polymer and other techniques. This paper proposes a novel and significant technique to increase the flexural capacity of simply supported reinforced concrete beams. The proposed method uses a new reinforcement bar system having bent-up bars, covered with rubber tubes. This technique will avoid interaction of bent-up bars with concrete. They are located in the zone where compressive and tensile forces act against one another. The compressive force in the upper point of the bent-up bars is exerted to the end point of these bars located under neutral axis. Moreover, the tensile stress is decreased in reinforcements located under the neutral axis. This will cause the Reinforced Concrete (RC) beam to endure extra loading before reaching yield stress. These factors may well be considered as reasons to increase bending capacity in the new system. The laboratory work together with finite element method analysis were carried out in this investigation. Furthermore, bending capacity, ductility, strength, and cracking zone were assessed for the new proposed system and compared with the conventional model. Both the FEM simulation and the experimental test results revealed that the proposed system has significant impact in increasing the load bearing capacity and the stiffness of the RC beams. In the present study, an equation is formulated to calculate bending capacity of a new reinforcement bar system beam.
The concept of structural robustness and relevant design guidelines have been in existence in the progressive collapse literature since the 1970s following the partial collapse of the Ronan Point apartment building; however, in the more general context, research on the evaluation and enhancement of structural robustness is still relatively limited. This paper is aimed to provide a general overview of the current state of research concerning structural robustness. The focus is placed on the quantification and the associated evaluation methodologies, rather than specific measures to ensure prescriptive robustness requirements. Some associated concepts, such as redundancy and vulnerability, will be discussed and interpreted in the general context of robustness such that the corresponding methodologies can be compared quantitatively using a comparable scale. A framework methodology proposed by the authors is also introduced in line with the discussion of the literature.
The smoothed finite element method (S-FEM) was originated by G R Liu by combining some meshfree techniques with the well-established standard finite element method (FEM). It has a family of models carefully designed with innovative types of smoothing domains. These models are found having a number of important and theoretically profound properties. This article first provides a concise and easy-to-follow presentation of key formulations used in the S-FEM. A number of important properties and unique features of S-FEM models are discussed in detail, including 1) theoretically proven softening effects; 2) upper-bound solutions; 3) accurate solutions and higher convergence rates; 4) insensitivity to mesh distortion; 5) Jacobian-free; 6) volumetric-locking-free; and most importantly 7) working well with triangular and tetrahedral meshes that can be automatically generated. The S-FEM is thus ideal for automation in computations and adaptive analyses, and hence has profound impact on AI-assisted modeling and simulation. Most importantly, one can now purposely design an S-FEM model to obtain solutions with special properties as wish, meaning that S-FEM offers a framework for design numerical models with desired properties. This novel concept of numerical model on-demand may drastically change the landscape of modeling and simulation. Future directions of research are also provided.