Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.

This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.

A theoretical solution is aimed to be developed in this research for predicting the failure in internally pressurized composite pressure vessels exposed to low-velocity impact. Both in-plane and out-of-plane failure modes are taken into account simultaneously and thus all components of the stress and strain fields are derived. For this purpose, layer-wise theory is employed in a composite cylinder under internal pressure and low-velocity impact. Obtained stress/strain components are fed into appropriate failure criteria for investigating the occurrence of failure. In case of experiencing any in-plane failure mode, the evolution of damage is modeled using progressive damage modeling in the context of continuum damage mechanics. Namely, mechanical properties of failed ply are degraded and stress analysis is performed on the updated status of the model. In the event of delamination occurrence, the solution is terminated. The obtained results are validated with available experimental observations in open literature. It is observed that the sequence of in-plane failure and delamination varies by increasing the impact energy.

In this study, the influences of spatially varying stochastic properties on free vibration analysis of composite plates were investigated via development of a new approach named the deterministic-stochastic Galerkin-based semi-analytical method. The material properties including tensile modulus, shear modulus, and density of the plate were assumed to be spatially varying and uncertain. Gaussian fields with first-order Markov kernels were utilized to define the aforementioned material properties. The stochastic fields were decomposed via application of the Karhunen-Loeve theorem. A first-order shear deformation theory was assumed, following which the displacement field was defined using admissible trigonometric modes to derive the potential and kinetic energies. The stochastic equations of motion of the plate were obtained using the variational principle. The deterministic-stochastic Galerkin-based method was utilized to find the probability space of natural frequencies, and the corresponding mode shapes of the plate were determined using a polynomial chaos approach. The proposed method significantly reduced the size of the mathematical models of the structure, which is very useful for enhancing the computational efficiency of stochastic simulations. The methodology was verified using a stochastic finite element method and the available results in literature. The sensitivity of natural frequencies and corresponding mode shapes due to the uncertainty of material properties was investigated, and the results indicated that the higher-order modes are more sensitive to uncertainty propagation in spatially varying properties.

In this paper, design scenarios of a tuned mass damper (TMD) for seismically excited structures are ranked. Accordingly, 10 design scenarios in two cases, namely unconstrained and constrained for the maximum TMD, are considered in this study. A free search of the TMD parameters is performed using a particle swarm optimization (PSO) algorithm for optimum tuning of TMD parameters. Furthermore, nine criteria are adopted with respect to functional, operational, and economic views. A technique for order performance by similarity to ideal solution (TOPSIS) is utilized for ranking the adopted design scenarios of TMD. Numerical studies are conducted on a 10-story building equipped with TMD. Simulation results indicate that the minimization of the maximum story displacement is the optimum design scenario of TMD for the seismic-excited structure in the unconstrained case for the maximum TMD stroke. Furthermore, H₂ of the displacement vector of the structure exhibited optimum ranking among the adopted design scenarios in the constrained case for the maximum TMD stroke. The findings of this study can be useful and important in the optimum design of TMD parameters with respect to functional, operational, and economic perspectives.

Once a column in building is removed due to gas explosion, vehicle impact, terrorist attack, earthquake or any natural disaster, the loading supported by removed column transfers to neighboring structural elements. If these elements are unable to resist the supplementary loading, they continue to fail, which leads to progressive collapse of building. In this paper, an efficient strategy to model and simulate the progressive collapse of multi-story reinforced concrete structure under sudden column removal is presented. The strategy is subdivided into several connected steps including failure mechanism creation, MBS dynamic analysis and dynamic contact simulation, the latter is solved by using conserving/decaying scheme to handle the stiff nonlinear dynamic equations. The effect of gravity loads, structure-ground contact, and structure-structure contact are accounted for as well. The main novelty in this study consists in the introduction of failure function, and the proper manner to control the mechanism creation of a frame until its total failure. Moreover, this contribution pertains to a very thorough investigation of progressive collapse of the structure under sudden column removal. The proposed methodology is applied to a six-story frame, and many different progressive collapse scenarios are investigated. The results illustrate the efficiency of the proposed strategy.

Research on the quality of data in a structural calculation document (SCD) is lacking, although the SCD of a bridge is used as an essential reference during the entire lifecycle of the facility. XML Schema matching enables qualitative improvement of the stored data. This study aimed to enhance the applicability of XML Schema matching, which improves the speed and quality of information stored in bridge SCDs. First, the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs. The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations. Second, the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree. The decision tree model was built using the content elements stored in the SCD, design companies, bridge types, and weight factors as input variables, and the matching accuracy as the target variable. The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.

We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models. Multiple linear regression, multiple log equation regression, gene expression programming, and combinations of these models are employed to predict the maximum turbulence, maximum velocity, resting area, and water depth of the middle pool in the fishway. The statistical parameters and error terms, including the coefficient of determination, root mean square error, normalized square error, maximum positive and negative errors, and mean absolute percentage error were employed to evaluate and compare the accuracy of the models. We also conducted a parametric study. The independent variables include the opening between baffles (OBB), the ratio of the length of the large and small baffles, the volume flow rate, and the angle of the large baffle. The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and OBB.

In this paper, we report the turbulent flow structures and the scour geometry around two piers with different diameters. An experiment was conducted on a non-uniform sand bed with two types of tandem arrangements, namely, pier (T1) with a 75 mm front and 90 mm rear, and pier (T2) with a 90 mm front and 75 mm rear, with and without-seepage flows, respectively. A strong wake region was observed behind the piers, but the vortex strength diminished with downward seepage. Streamwise velocity was found to be maximum near the bed downstream of the piers and at the edge of the scour hole upstream of the piers. Quadrant analysis was used to recognize the susceptible region for sediment entrainment and deposition. Upstream of the piers near the bed, the moments, turbulent kinetic energy (TKE), and TKE fluxes were found to decrease with downward seepage, in contrast to those in a plane mobile bed without piers. The reduction percentages of scour depth at the rear pier compared with the front one were approximately 40% for T1 and 60% for T2. Downward seepage also resulted in restrained growth of scouring with time.

Slope failure occurs due to an increase in the saturation level and a subsequent decrease in matric suction in unsaturated soil. This paper presents the results of a series of centrifuge experiments and numerical analyses on a 55° inclined unsaturated sandy slope with less permeable, stronger silty sand layer inclusion within it. It is observed that a less permeable, stronger silty sand layer in an otherwise homogeneous sandy soil slope hinders the infiltration of water. The water content of the slope just above the stronger layer increases significantly, compared to elsewhere. No shear band is found to initiate in a homogeneous sandy soil slope, whereas for a non-homogeneous slope, they initiate just above the less pervious, stronger layer. A discontinuity of the shear zone is also observed for the case of a non-homogeneous soil slope. The factor of safety of a non-homogeneous, unsaturated soil slope decreases because of the less permeable, stronger layer. It decreases significantly if this less permeable, stronger soil layer is located near the toe of the slope.

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

Natural slopes consist of non-homogeneous soil profiles with distinct characteristics from slopes made of homogeneous soil. In this study, the limit equilibrium modified pseudo-dynamic method is used to analyze the stability of two-layered c-φ soil slopes in which the failure surface is assumed to be a logarithmic spiral. The zero-stress boundary condition at the ground surface under the seismic loading condition is satisfied. New formulations derived from an analytical method are proposed for the predicting the seismic response in two-layered soil. A detailed parametric study was performed in which various parameters (seismic accelerations, damping, cohesion, and angle of internal friction) were varied. The results of the present method were compared with those in the available literature. The present analytical analysis was also verified against the finite element analysis results.

Permeability is a major indicator of concrete durability, and depends primarily on the microstructure characteristics of concrete, including its porosity and pore size distribution. In this study, a variety of concrete samples were prepared to investigate their microstructure characteristics via nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and X-ray computed tomography (X-CT). Furthermore, the chloride diffusion coefficient of concrete was measured to explore its correlation with the microstructure of the concrete samples. Results show that the proportion of pores with diameters<1000 nm obtained by NMR exceeds that obtained by MIP, although the difference in the total porosity determined by both methods is minimal. X-CT measurements obtained a relatively small porosity; however, this likely reflects the distribution of large pores more accurately. A strong correlation is observed between the chloride diffusion coefficient and the porosity or contributive porosity of pores with sizes<1000 nm. Moreover, microstructure parameters measured via NMR reveal a lower correlation coefficient R² versus the chloride diffusion coefficient relative to the parameters determined via MIP, as NMR can measure non-connected as well as connected pores. In addition, when analyzing pores with sizes>50 µm, X-CT obtains the maximal contributive porosity, followed by MIP and NMR.

In this study, for the first time, we investigate the nonlocality superimposed to the size effects on the nonlinear dynamics of an electrically actuated single-walled carbon-nanotube-based resonator. We undertake two models to capture the nanostructure nonlocal size effects: the strain and the velocity gradient theories. We use a reduced-order model based on the differential quadrature method (DQM) to discretize the governing nonlinear equation of motion and acquire a discretized-parameter nonlinear model of the system. The structural nonlinear behavior of the system assuming both strain and velocity gradient theories is investigated using the discretized model. The results suggest that nonlocal and size effects should not be neglected because they improve the prediction of corresponding dynamic amplitudes and, most importantly, the critical resonant frequencies of such nanoresonators. Neglecting these effects may impose a considerable source of error, which can be amended using more accurate modeling techniques.

Some of the current concrete damage plasticity models in the literature employ a single damage variable for both the tension and compression regimes, while a few more advanced models employ two damage variables. Models with a single variable have an inherent difficulty in accounting for the damage accrued due to tensile and compressive actions in appropriately different manners, and their mutual dependencies. In the current models that adopt two damage variables, the independence of these damage variables during cyclic loading results in the failure to capture the effects of tensile damage on the compressive behavior of concrete and vice-versa. This study presents a cyclic model established by extending an existing monotonic constitutive model. The model describes the cyclic behavior of concrete under multiaxial loading conditions and considers the influence of tensile/compressive damage on the compressive/tensile response. The proposed model, dubbed the enhanced concrete damage plasticity model (ECDPM), is an extension of an existing model that combines the theories of classical plasticity and continuum damage mechanics. Unlike most prior studies on models in the same category, the performance of the proposed ECDPM is evaluated using experimental data on concrete specimens at the material level obtained under cyclic multiaxial loading conditions including uniaxial tension and confined compression. The performance of the model is observed to be satisfactory. Furthermore, the superiority of ECDPM over three previously proposed constitutive models is demonstrated through comparisons with the results of a uniaxial tension-compression test and a virtual test.

Self-consolidating concrete (SCC) with manufactured sand (MSCC) is crucial to guarantee the quality of concrete construction technology and the associated property. The properties of MSCC with different microlimestone powder (MLS) replacements of retreated manufactured sand (TMsand) are investigated in this study. The result indicates that high-performance SCC, made using TMsand (TMSCC), achieved high workability, good mechanical properties, and durability by optimizing MLS content and adding fly ash and silica fume. In particular, the TMSCC with 12% MLS content exhibits the best workability, and the TMSCC with 4% MLS content has the highest strength in the late age, which is even better than that of SCC made with the river sand (Rsand). Though MLS content slightly affects the hydration reaction of cement and mainly plays a role in the nucleation process in concrete structures compared to silica fume and fly ash, increasing MLS content can evidently have a significant impact on the early age hydration progress. TMsand with MLS content ranging from 8% to 12% may be a suitable alternative for the Rsand used in the SCC as fine aggregate. The obtained results can be used to promote the application of SCC made with manufactured sand and mineral admixtures for concrete-based infrastructure.

The physio-chemical changes in concrete mixes due to different coarse aggregate (natural coarse aggregate and recycled coarse aggregate (RCA)) and mix design methods (conventional method and Particle Packing Method (PPM)) are studied using thermogravimetric analysis of the hydrated cement paste. A method is proposed to estimate the degree of hydration (\alpha) from chemically bound water (W_{\mathrm{B}}). The PPM mix designed concrete mixes exhibit lower \alpha. Recycled aggregate concrete (RAC) mixes exhibit higher and \alpha after 7 d of curing, contrary to that after 28 and 90 d. The chemically bound water at infinite time (W_{\mathrm{B}\infty }) of RAC mixes are lower than the respective conventional concrete mixes. The lower W_{\mathrm{B}\infty }, Ca(OH)₂ bound water, free Ca(OH)₂ content and FT-IR analysis substantiate the use of pozzolanic cement in the parent concrete of RCA. The compressive strength of concrete and \alpha cannot be correlated for concrete mixes with different aggregate type and mix design method as the present study confirms that the degree of hydration is not the only parameter which governs the macro-mechanical properties of concrete. In this regard, further study on the influence of interfacial transition zone, voids content and aggregate quality on macro-mechanical properties of concrete is needed.

Despite the large variations in the behaviors of water-reducing admixtures upon changes in their structures, most previous reports on the cement-admixture compatibility did not provide sufficient information on the structure of the admixture. Hence, the evaluation and generalization of the reports on the cement-admixture compatibility are challenging. In this study, three different polycarboxylate-ether-based water-reducing admixtures with the same free nonionic content, anionic/nonionic molar ratio, and main chain length and different side chain lengths were produced. The compatibility of these admixtures with a CEM I 42.5R-type cement was investigated. In addition, an analysis of variance was performed on the experiment results to evaluate the contributions of the admixture type, admixture/cement ratio, and elapsing time to the Marsh funnel flow time, mini-slump, slump flow, and compressive strength. The water-reducing admixtures having long or short side chains reduced the initial flow characteristics of the cementitious systems. However, the admixture having the shortest side chain was better with regard to flow retention. The side chain length of the admixture did not have significant effects on the compressive strength and water absorption capacity of the mortar mixtures and mini-slump performances of the cement paste mixtures. Regarding the behaviors of the admixtures in the cementitious systems, an optimal admixture side chain molecular weight is proposed.