Frontiers of Structural and Civil Engineering最新文献

The presence of water films on a runway surface presents a risk to the landing of aircraft. The tire of the aircraft is separated from the runway due to a hydrodynamic force exerted through the water film, a phenomenon called hydroplaning. Although a lot of numerical investigations into hydroplaning have been conducted, only a few have considered the impact of the runway permeability. Hence, computational problems, such as excessive distortion and computing efficiency decay, may arise with such numerical models when dealing with the thin water film. This paper presents a numerical model comprising of the tire, water film, and the interaction with the runway, applying a mathematical model using the smoothed particle hydrodynamics and finite element (SPH-FE) algorithm. The material properties and geometric features of the tire model were included in the model framework and water film thicknesses from 0.75 mm to 7.5 mm were used in the numerical simulation. Furthermore, this work investigated the impacts of both surface texture and the runway permeability. The interaction between tire rubber and the rough runway was analyzed in terms of frictional force between the two bodies. The SPH-FE model was validated with an empirical equation proposed by the National Aeronautics and Space Administration (NASA). Then the computational efficiency of the model was compared with the traditional coupled Eulerian-Lagrangian (CEL) algorithm. Based on the SPH-FE model, four types of the runway (Flat, SMA-13, AC-13, and OGFC-13) were discussed. The simulation of the asphalt runway shows that the SMA-13, AC-13, and OGFC-13 do not present a hydroplaning risk when the runway permeability coefficient exceeds 6%.

Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete (RC) members in torsion, torsional mechanism exploration and torsional performance prediction have always been difficult. In the present paper, several machine learning models were applied to predict the torsional capacity of RC members. Experimental results of a total of 287 torsional specimens were collected through an overall literature review. Algorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificial neural network and support vector machine, were trained and tested by 10-fold cross-validation method. Predictive performances of proposed machine learning models were evaluated and compared, both with each other and with the calculated results of existing design codes, i.e., GB 50010, ACI 318-19, and Eurocode 2. The results demonstrated that better predictive performance was achieved by machine learning models, whereas GB 50010 slightly overestimated the torsional capacity, and ACI 318-19 and Eurocode 2 underestimated it, especially in the case of ACI 318-19. The XGBM model gave the most favorable predictions with R² = 0.999, RMSE = 1.386, MAE = 0.86, and (bar{lambda}=0.976). Moreover, strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model, followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks (DNNs). A significant challenge in this field is the limited availability of measurement data for full-scale structures, which is addressed in this paper by generating data sets using a reduced finite element (FE) model constructed by SAP2000 software and the MATLAB programming loop. The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices. The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios. To achieve the most generalized surrogate model, the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process. The approach’s effectiveness, efficiency, and applicability are demonstrated by two numerical examples. The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data. Overall, the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms, which can be computationally intensive. This approach also shows potential for broader applications in structural damage detection.

This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects, including lack of the fusion, porosity, slag inclusion, and the qualified (no defects) cases. This methodology solves the shortcomings of existing detection methods, such as expensive equipment, complicated operation and inability to detect internal defects. The study first collected percussed data from welded steel members with or without weld defects. Then, three methods, the Mel frequency cepstral coefficients, short-time Fourier transform (STFT), and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses. Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted features. Furthermore, experiments were designed and performed to validate the proposed method. Results showed that STFT achieved higher accuracies (up to 96.63% on average) in the weld status classification. The convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms with an average accuracy of 95.8%. In addition, random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

The brittleness of cement composites makes cracks almost inevitable, producing a serious limitation on the lifespan, resilience, and safety of concrete infrastructure. To address this brittleness, self-healing concrete has been developed for regaining its mechanical and durability properties after becoming cracked, thereby promising sustainable development of concrete infrastructure. This paper provides a comprehensive review of the latest developments in self-healing concrete. It begins by summarizing the methods used to evaluate the self-healing efficiency of concrete. Next, it compares strategies for achieving healing concrete. It then discusses the typical approaches for developing self-healing concrete. Finally, critical insights are proposed to guide future studies on the development of novel self-healing concrete. This review will be useful for researchers and practitioners interested in the field of self-healing concrete and its potential to improve the durability, resilience, and safety of concrete infrastructure.

This study investigated the application of electrical resistance tomography (ERT) in characterizing the slurry spatial distribution in cemented granular materials (CGMs). For CGM formed by self-flow grouting, the voids in the accumulation are only partially filled and the bond strength is often limited, which results in difficulty in obtaining in situ samples for quality evaluation. Therefore, it is usually infeasible to evaluate the grouting effect or monitor the slurry spatial distribution by a mechanical method. In this research, the process of grouting cement paste into high alumina ceramic beads (HACB) accumulation is reliably monitored with ERT. It shows that ERT results can be used to calculate the cement paste volume in the HACB accumulation, based on calibrating the saturation exponent n in Archie’s law. The results support the feasibility of ERT as an imaging tool in CGM characterization and may provide guidance for engineering applications in the future.

The hazardous environmental effects of greenhouse gas emissions and climate change demand alternative sources for cementitious materials in the construction industry. The development of geopolymer structures provides a way of producing 100% cement-free construction. In this research work, a novel and simple way of deriving nano particles from waste fly ash particles is promoted. The effect of adding the synthesized nano fly ash particles as a filler medium in geopolymer mortars was investigated by considering strength and durability properties. Parameter optimization was done by using regression analysis on the geopolymer mortar and the impact of adding nano fly ash particles was studied by varying different percentages of addition ranging from 0 to 7.5% by weight of binder content. From the results, it was observed that 1% nano fly ash acted not only as a filler but also as nano-sized precursors of the polymerization process, resulting in denser geopolymer medium. This can explain the extraordinary gain in strength of 72.11 MPa as well as the denser core with negligible level of chloride ion penetration, making the material suitable for the development of structures susceptible to marine environment.

Robotic-based technologies such as automated spraying or extrusion-based 3-dimensional (3D) concrete printing can be used to build tunnel linings, aiming at reducing labor and mitigating the associated safety issues, especially in the high-geothermal environment. Extrusion-based 3D concrete printing (3DCP) has additional advantages over automated sprayings, such as improved surface quality and no rebound. However, the effect of different temperatures on the adhesion performance of 3D-printed materials for tunnel linings has not been investigated. This study developed several alkali-activated slag mixtures with different activator modulus ratios to avoid the excessive use of Portland cement and enhance sustainability of 3D printable materials. The thermal responses of the mixtures at different temperatures of 20 and 40 °C were studied. The adhesion strength of the alkali-activated material was evaluated for both early and later ages. Furthermore, the structural evolution of the material exposed to different temperatures was measured. This was followed by microstructure characterization. Results indicate that elevated temperatures accelerate material reactions, resulting in improved early-age adhesion performance. Moreover, higher temperatures contribute to the development of a denser microstructure and enhanced mechanical strength in the hardened stage, particularly in mixtures with higher silicate content.

Steel structures are widely used; however, their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective. Therefore, a multi-population particle swarm optimization (MPPSO) algorithm is developed to optimize the weight of steel frames according to standard design codes. Modifications are made to improve the algorithm performances including the constraint-based strategy, piecewise mean learning strategy and multi-population cooperative strategy. The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes. The related parameter influences on optimization results are discussed. For the representative frame, MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4% lighter. A similar trend can also be observed in cases subjected to Chinese design codes. In addition, a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results. The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes. It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity. The proposed method can be easily generalized to the optimization problem of other structural systems.

The utilization of recycled aggregates (RA) for concrete production has the potential to offer substantial environmental and economic advantages. However, RA concrete is plagued with considerable durability concerns, particularly carbonation. To advance the application of RA concrete, the establishment of a reliable model for predicting the carbonation is needed. On the one hand, concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor. On the other hand, carbonation is influenced by many factors and is hard to predict. Regarding this, this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth (CD) of RA concrete. Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools. It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer (XGB-MVO) with R² value of 0.9949 and 0.9398 for training and testing sets, respectively. XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated. It also showed better generalization capabilities when compared with different models in the literature. Overall, this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.