Sufficient survey data are required to describe the stochastic behaviors of live loads. However, due to manual and on-site operation required by traditional survey methods, traditional surveys face challenges like occupant resistance, high costs, and long implementation periods. This study proposes a new survey method to access live load data online and automatically. Required samples are acquired from multi-source, open-access and dynamically updated data on the Internet. The change intervals, geometrical dimensions and object quantities are obtained from transaction information, building attributes and virtual reality models on real estate websites, respectively. The object weights are collected from commodity information on e-commerce websites. The integration of the aforementioned data allows for the extraction of necessary statistics to describe a live load process. The proposed method is applied to a live load survey in China, covering 20040 m2, with around 90000 samples acquired for object weights and load changes. The survey results reveal that about 70%−80% of the amplitude statistics are attributable to 1/6 of the total object types.
The Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool.
This study employs a hybrid approach, integrating finite element method (FEM) simulations with machine learning (ML) techniques to investigate the structural performance of double-skin tubular columns (DSTCs) reinforced with glass fiber-reinforced polymer (GFRP). The investigation involves a comprehensive examination of critical parameters, including aspect ratio, concrete strength, number of GFRP confinement layers, and dimensions of steel tubes used in DSTCs, through comparative analyses and parametric studies. To ensure the credibility of the findings, the results are rigorously validated against experimental data, establishing the precision and trustworthiness of the analysis. The present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.
The present work aims to develop an object tracking controller for the Stewart platform using a computer vision-assisted machine learning-based approach. This research is divided into two modules. The first module focuses on the design of a motion controller for the Physik Instrumente (PI)-based Stewart platform. In contrast, the second module deals with the development of a machine-learning-based spatial object tracking algorithm by collecting information from the Zed 2 stereo vision system. Presently, simple feed-forward neural networks (NN) are used to predict the orientation of the top table of the platform. While training, the x, y, and z coordinates of the three-dimensional (3D) object, extracted from images, are used as the input to the NN. In contrast, the orientation information of the platform (that is, rotation about the x, y, and z-axes) is considered as the output from the network. The orientation information obtained from the network is fed to the inverse kinematics-based motion controller (module 1) to move the platform while tracking the object. After training, the optimised NN is used to track the continuously moving 3D object. The experimental results show that the developed NN-based controller has successfully tracked the moving spatial object with reasonably good accuracy.
This study focuses on exploring the effects of geometrical imperfections and different analysis methods on the optimum design of Double-Layer Grids (DLGs), as used in the construction industry. A total of 12 notable meta-heuristics are assessed and contrasted, and as a result, the Slime Mold Algorithm is identified as the most effective approach for size optimization of DLGs. To evaluate the influence of geometric imperfections and nonlinearity on the optimal design of real-size DLGs, the optimization process is carried out by considering and disregarding geometric nonlinearity while incorporating three distinct forms of geometrical imperfections, namely local imperfections, global imperfections, and combinations of both. In light of the uncertain nature of geometrical imperfections, probabilistic distributions are used to define these imperfections randomly in direction and magnitude. The results demonstrate that it is necessary to account for these imperfections to obtain an optimal solution. It’s worth noting that structural imperfections can increase the maximum stress ratio by up to 70%. The analysis also reveals that the initial curvature of members has a more significant impact on the optimal design of structures than the nodal installation error, indicating the need for greater attention to local imperfection issues in space structure construction.
The use of three-dimensional (3D) printed concrete as formwork is becoming more widely applied within the industry. However, the technology is still not optimized and there are many reports of preliminary cracking during the curing of cast concrete. This is believed to result from differential shrinkage between the printed and cast concrete. These cracks (in the printed concrete or at the interface between the infill and printed concrete) form a preferential path for aggressive substances and can reduce the durability of the combined concrete element. To ensure the desired service life of the structure, it is important that the differential shrinkage between cast and printed concrete is understood. This study investigated the effect of curing conditions on the differential shrinkage behavior of 3D and cast concrete. The influence of prewetting of the dry-cured 3D printed formwork was also determined. In the experimental program, a vibrated and self-compacting concrete were used as cast material. Linear 3D printed formwork was produced and combined with cast concrete to simulate a concrete structure. Printed formwork was cured for 1, 7, or 28 d exposed to the air (relative humidity: 60% or 95%) or submerged in water. The length change of the combined elements was observed over 56 d after concrete casting and throughout the thickness of the materials. Results show that increasing the curing period in dry conditions of the printed concrete leads to an expansion of the formwork on the first day after casting. The expansion leads to a non-uniform strain evolution throughout the curing period of the combined element. Printed concrete formwork stored in wet conditions does not expand after the casting process but tends to show a decreasing linear deformation within the whole elements.
Recently developed multi-scale fiber (i.e., CaCO3 whisker, polyvinyl alcohol (PVA) fiber, and steel fiber) reinforced rubberized concrete exhibits excellent mechanical properties and spalling resistance at high temperatures. Measurement of macro properties such as strength and Young’s modulus cannot reveal and characterize damage mechanisms, particularly those relating to the multi-scale fiber strengthening effect. In this study, acoustic emission (AE) technology is applied to investigate the impact of multi-scale fiber on the damage evolution of rubberized concrete exposed to high temperatures, under the uniaxial compression and tension loading processes. The mechanical properties, AE event location, peak frequency, b-value, the ratio of rise time to amplitude (RA), average frequency (AF) values, and AE energy of specimens are investigated. The results show that the number of events observed using AE gradually increases as the loading progresses. The crumb rubber and fibers inhibit the generation and development of the cracks. It is concluded that both the peak frequency and b-value reflect the extension process of cracks. As the cracks develop from the micro scale to the macro scale, the peak frequency tends to be distributed in a lower frequency range, and the b-value decreases gradually. At the peak stress point, the AE energy increases rapidly and the b-value decreases. The specimens without multi-scale fibers exhibit brittle failure, while the specimens with fibers exhibit ductile failure. In addition, adding multi-scale fibers and crumb rubber increases the peak frequency in the medium and high frequency ranges, indicating a positive effect on inhibiting crack development. After being subjected to high temperatures, the maximum and minimum b-values decrease, reflecting an increase in the number of initial cracks due to thermal damage. Meanwhile, the RA and AF values are used to classify tensile and shear cracks. The specimens fracture with more shear cracks under compression, and there are more tensile cracks in specimens with multi-scale fibers under tension.
The harsh environment in tunnels with high geothermal temperatures and humidity can adversely impact machinery, personnel, and construction. The main causes of specific problems are the unknown mechanisms of local geothermal formation, inappropriate temperature control measures, and insufficient systematic safeguards. In this study, three work sections relating to a high geothermal tunnel are: the tunnel face, middle-of-tunnel section, and outside-of-tunnel section. A cooling strategy is proposed to offer technical support in achieving comprehensive cooling, overall as well as for each of the sections. First, a comprehensive geological survey explores the mechanism and exact location of the heat source. Secondly, grouting and centralized drainage measures are used to control the heat release of hot water. Enhanced ventilation, ice chillers and other applicable measures are used to control the ambient temperature. Finally, a monitoring and early warning system is established to prevent accidents. This cooling strategy has been applied in the field with good results.
The stick-slip action of strike-slip faults poses a significant threat to the safety and stability of underground structures. In this study, the north-east area of the Longmenshan fault, Sichuan, provides the geological background; the rheological characteristics of the crustal lithosphere and the nonlinear interactions between plates are described by Burger’s viscoelastic constitutive model and the friction constitutive model, respectively. A large-scale global numerical model for plate squeezing analysis is established, and the seemingly periodic stick-slip action of faults at different crust depths is simulated. For a second model at a smaller scale, a local finite element model (sub-model), the time history of displacement at a ground level location on the Longmenshan fault plane in a stick-slip action is considered as the displacement loading. The integration of these models, creating a multi-scale modeling method, is used to evaluate the crack propagation and mechanical response of a tunnel subjected to strike-slip faulting. The determinations of the recurrence interval of stick-slip action and the cracking characteristics of the tunnel are in substantial agreement with the previous field investigation and experimental results, validating the multi-scale modeling method. It can be concluded that, regardless of stratum stiffness, initial cracks first occur at the inverted arch of the tunnel in the footwall, on the squeezed side under strike-slip faulting. The smaller the stratum stiffness is, the smaller the included angle between the crack expansion and longitudinal direction of the tunnel, and the more extensive the crack expansion range. For the tunnel in a high stiffness stratum, both shear and bending failures occur on the lining under strike-slip faulting, while for that in the low stiffness stratum, only bending failure occurs on the lining.
A combined deep machine learning (DML) and collocation based approach to solve the partial differential equations using artificial neural networks is proposed. The developed method is applied to solve problems governed by the Sine–Gordon equation (SGE), the scalar wave equation and elasto-dynamics. Two methods are studied: one is a space-time formulation and the other is a semi-discrete method based on an implicit Runge–Kutta (RK) time integration. The methodology is implemented using the Tensorflow framework and it is tested on several numerical examples. Based on the results, the relative normalized error was observed to be less than 5% in all cases.
