Research

11 pages, 2562 KiB

Open AccessArticle

Mechanical and Thermal Conductivity Study of Inorganic Modified Raw Soil Materials Based on Gradient Concept

by Fei Yang, Mengjie Qiao, Linchang Li, Yangyang Cui and Junxia Liu

Buildings 2023, 13(9), 2155; https://doi.org/10.3390/buildings13092155 - 25 Aug 2023

Viewed by 471

Based on the inorganic modification of raw soil materials by using cement, fly ash, lime and other admixtures, the influence of modified jute fiber content on the strength of raw-soil-based wall materials was studied. The effects of the gradient distribution of inorganic admixtures [...] Read more.

Based on the inorganic modification of raw soil materials by using cement, fly ash, lime and other admixtures, the influence of modified jute fiber content on the strength of raw-soil-based wall materials was studied. The effects of the gradient distribution of inorganic admixtures on the mechanical properties and thermal conductivity of raw-soil-based wall materials were studied and compared by designing the gradient distribution and homogenous distribution of admixtures in raw soil materials. The results show that when the mass ratio of raw soil, sand, cement, fly ash and lime is 30:10:5:3:2, the compressive strength and flexural strength of the modified raw soil specimen at 28 d are 6.4 MPa and 2.9 MPa, respectively; on the basis of the further addition of 0.8 v% jute fibers, the strength can still be enhanced by 20% and its thermal insulation properties will also be improved. Gradient design can further improve the mechanical properties of modified raw-soil-based wall materials and can weaken the loss of its inherent thermal insulation function. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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17 pages, 7716 KiB

Open AccessArticle

A Case Study on the Application of 3D Scanning Technology in Deformation Monitoring of Slope Stabilization Structure

by Fengxiao Yu, Jianpeng Tong, Yipu Peng, Li Chen and Shuangyu Wang

Buildings 2023, 13(7), 1589; https://doi.org/10.3390/buildings13071589 - 23 Jun 2023

Viewed by 1148

Abstract

Traditional deformation monitoring suffers from issues such as the point-based representation of surfaces and low measurement efficiency. Moreover, the majority of researchers study the deformation of slopes using methods such as 3S technology, synthetic aperture radar interferometry, distributed fiber optic sensing technology, etc. [...] Read more.

Traditional deformation monitoring suffers from issues such as the point-based representation of surfaces and low measurement efficiency. Moreover, the majority of researchers study the deformation of slopes using methods such as 3S technology, synthetic aperture radar interferometry, distributed fiber optic sensing technology, etc. Based on this, a slope stabilization structure deformation monitoring method based on 3D laser scanning technology is proposed. First, with the slope stabilization structure of Caihong Road as the engineering background, point cloud data of the slope stabilization structure is obtained using a Trimble SX10 device. Second, the point deformation, overall deformation, and line deformation of the two-phase slope stabilization structure point cloud data are analyzed. Finally, the measurement accuracy of the 3D laser scanning technology is evaluated. The results show that the deformation analysis of points, lines, and surfaces can complement each other, thereby comprehensively assessing the situation of slope stabilization structure deformation. Moreover, the maximum displacement value in the deformation of points, lines, and surfaces is 8.52 mm, which does not exceed the standard, and 93.61% of the point deformation is between −0.76~0.92 mm, indicating that the slope stabilization structure is in a safe and stable state. The independent sample t-test has a test statistic of t = 2.074, verifying that the 3D laser scanning technology and the total station measurement accuracy are highly consistent and can meet the needs of actual engineering. The results of this study can provide a reasonable theoretical and methodological reference for analyzing similar engineering deformation monitoring in the future. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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13 pages, 5041 KiB

Open AccessArticle

Mechanical Analysis Model of Asphalt Concrete Random Particles Based on Mix Ratio

by Jianguo Jiang, Xihe Zhang and Xiaoming Liu

Buildings 2023, 13(7), 1583; https://doi.org/10.3390/buildings13071583 - 21 Jun 2023

Cited by 1 | Viewed by 779

Abstract

The strength and other mechanical properties of asphalt mixtures are directly affected by their size, shape, distribution, and interaction of asphalt concrete particles. Mesoscale performance is important for studying the structure of asphalt mixtures. The random particle model of asphalt concrete is one [...] Read more.

The strength and other mechanical properties of asphalt mixtures are directly affected by their size, shape, distribution, and interaction of asphalt concrete particles. Mesoscale performance is important for studying the structure of asphalt mixtures. The random particle model of asphalt concrete is one of the important topics in mesoscale research. On the basis of existing research at home and abroad, based on the asphalt mixture mix ratio, this paper further improves the algorithm of random particle generation, optimizes the algorithm of single-particle generation, ensures that the shape of particles at the time of generation conforms to the engineering practice, and forms a random particle reserve library, in order to improve the efficiency of particle delivery, facilitate the model mesh division, and provide a basis for the smooth progress of mechanical analysis. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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13 pages, 4575 KiB

Open AccessArticle

Shear Capacity Prediction Model of Deep Beam Based on New Hybrid Intelligent Algorithm

by Haibo Wang, Chen Zhang and Hengxuan Wu

Buildings 2023, 13(6), 1395; https://doi.org/10.3390/buildings13061395 - 27 May 2023

Cited by 1 | Viewed by 962

Abstract

Accurate shear load capacity predictions are crucial to achieving the load-bearing requirements of concrete deep beams in a variety of construction structures. Conventional BP neural networks have the drawbacks of being prone to local optimums and having a sluggish rate of convergence for [...] Read more.

Accurate shear load capacity predictions are crucial to achieving the load-bearing requirements of concrete deep beams in a variety of construction structures. Conventional BP neural networks have the drawbacks of being prone to local optimums and having a sluggish rate of convergence for predicting the shear load capacity of reinforced concrete deep beams. To overcome this problem, this study incorporated the black widow optimization algorithm (BWO) and principal component analysis (PCA) into a BP neural network to create a unique Hybrid Intelligent Optimization Algorithm (PCA-BWO-BP). Firstly, PCA was used to reduce the dimensionality of the input variables of the shear load capacity prediction model of reinforced concrete deep beams. Secondly, BWO was introduced to optimize the weights and thresholds of the BP neural network. Finally, the four algorithms were compared and validated through the use of five model evaluators. The results showed that the PCA-BWO-BP model can explore the intrinsic relationship between member size, bottom longitudinal reinforcement, hoop reinforcement, concrete strength and the shear load capacity of reinforced concrete deep beams and generate reasonable prediction values, and the complexity of the prediction model can be effectively reduced by introducing the PCA algorithm, whereas the BWO algorithm can optimize the weights and thresholds of the BP neural network to improve the convergence and global search ability of the model. The mean absolute percentage error (MAPE) of the PCA-BWO-BP algorithm is 5.126, and the Nash efficiency coefficient (NS) is 0.989. The generalization ability and prediction accuracy are significantly better than those of the BP neural network, which can solve the problem relating to the fact that BP neural networks are prone to falling into the local optimum. The PCA-BWO-BP model has strong prediction ability, stability, generalization ability and robustness, which can predict the shear load capacity of reinforced concrete deep beams more accurately. It provides a new method and case support for further research on the shear bearing capacity of reinforced concrete deep beams. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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19 pages, 5966 KiB

Open AccessArticle

Development of an IRMO-BPNN Based Single Pile Ultimate Axial Bearing Capacity Prediction Model

by Liangxing Jin and Yujie Ji

Buildings 2023, 13(5), 1297; https://doi.org/10.3390/buildings13051297 - 16 May 2023

Viewed by 587

Abstract

The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in pile design. BP neural network (BPNN) has a strong nonlinear mapping ability and can effectively predict the UABC of a single pile. However, frequent immersion in unstable search [...] Read more.

The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in pile design. BP neural network (BPNN) has a strong nonlinear mapping ability and can effectively predict the UABC of a single pile. However, frequent immersion in unstable search results with local vibration leads BPNN to a less usable solution. The weights and biases of the BPNN model are optimized using the improved radial movement optimization (IRMO) algorithm in this study, and a new method named the IRMO-BP neural network (IRMO-BPNN) is proposed to predict the UABC of a single pile. The IRMO-BPNN model was developed from a database of 196 static load test (SLT) samples, and model hyper-parameter analysis was carried out to determine the optimal number of hidden nodes, population size, and the number of iterations. The prediction accuracy and stability of the IRMO-BPNN model are verified by comparing it with the GA-based ANN model, ANFIS-GMDH-PSO model, and RBFANN model. The results show that the IRMO-BPNN model can accurately predict the UABC of a single pile and improves the situation that the BPNN model is easy to fall into local optimal values and its search results are unstable. The IRMO-BPNN model has significant advantages over other models. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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20 pages, 8152 KiB

Open AccessArticle

Influence of Fatigue Damage on Criticality Cell Ultimate Load Capacity of Steel–Concrete Composite Section

by Haibo Wang and Shasha Wu

Buildings 2023, 13(5), 1254; https://doi.org/10.3390/buildings13051254 - 10 May 2023

Cited by 1 | Viewed by 877

Abstract

In order to investigate the impact of fatigue damage on the ultimate load capacity of criticality cells in steel–concrete composite segments and to address the complex design challenges associated with bridge steel–concrete composite segments in practical engineering, this study designs two scaled criticality [...] Read more.

In order to investigate the impact of fatigue damage on the ultimate load capacity of criticality cells in steel–concrete composite segments and to address the complex design challenges associated with bridge steel–concrete composite segments in practical engineering, this study designs two scaled criticality cell specimens with a scale ratio of 1:2 and performs ultimate load capacity tests after fatigue cyclic loading. By analyzing the stress distribution of each component and the force transmission ratio and combining the results from finite element model calculations, this study introduces the degree of structural fatigue damage and proposes a predictive model for the ultimate load capacity of steel–concrete composite segment criticality cells that is easy to apply. This model is compared with the finite element calculation results and experimental values, and the results are found to be in good agreement. Additionally, the number of shear connection members in the model is optimized based on the calculation results. The research findings indicate that the main failure mode of criticality cells is inclined compression failure. The strength of each part decreases in the following order: steel–concrete composite segment, steel structure segment, and concrete segment. Furthermore, fatigue damage has a significant impact on criticality cells. The optimized model exhibits similar stress performance and force transmission ratio to the original model and provides a reference for the design of practical engineering. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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26 pages, 8872 KiB

Open AccessArticle

A New Kinematical Admissible Translational–Rotational Failure Mechanism Coupling with the Complex Variable Method for Stability Analyses of Saturated Shallow Square Tunnels

by Zhong-Zheng Peng and Ze-Hang Qian

Buildings 2023, 13(5), 1246; https://doi.org/10.3390/buildings13051246 - 09 May 2023

Viewed by 986

Abstract

Tunnels are commonly constructed in water-bearing zones, which necessitates stability analyses of saturated tunnels based on the upper bound of the plastic theory. Previous kinematical approaches have the following drawbacks: (1) using an empirical approach to estimate pore-water pressure distributions; (2) using failure [...] Read more.

Tunnels are commonly constructed in water-bearing zones, which necessitates stability analyses of saturated tunnels based on the upper bound of the plastic theory. Previous kinematical approaches have the following drawbacks: (1) using an empirical approach to estimate pore-water pressure distributions; (2) using failure mechanisms that are not rigorously kinematically admissible. To overcome these shortcomings, we proposed a rigorously kinematically admissible translational–rotational failure mechanism for an underwater shallow square tunnel where velocity discontinuity surfaces were derived. Then, the pore-water pressure field surrounding the tunnel under the boundary of constant water pressure is analytically generated based on the complex variable method and imported into the kinematically admissible velocity field. Work rates performed by external forces and the internal dissipation rate are numerically computed to formulate the power balance equation, followed by a mixed optimization algorithm to capture the critical states of the surrounding soils of tunnels. The outcomes of pore-water pressure distributions, safety factors, and failure mechanisms are in tandem with those given by the numerical simulation but show higher computational efficiency than the numerical simulation. In the end, we highlight the advantages of the proposed model over the empirical approach, where soil properties and water table elevation effects are analyzed. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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18 pages, 10280 KiB

Open AccessArticle

An Efficient Construction Method of the 3D Random Asphalt Concrete Model Based on the Background Grid and the Moving-and-Densifying Algorithm

by Xiaoming Liu, Huaan Chen and Yu Zhao

Buildings 2023, 13(4), 990; https://doi.org/10.3390/buildings13040990 - 08 Apr 2023

Viewed by 1327

Abstract

In order to avoid the tedious and time-consuming measuring process for thermal conductivity, many random models have been proposed, but the construction of those random models is still inefficient, which limits the further application. In this paper, a construction method of three-dimensional random [...] Read more.

In order to avoid the tedious and time-consuming measuring process for thermal conductivity, many random models have been proposed, but the construction of those random models is still inefficient, which limits the further application. In this paper, a construction method of three-dimensional random asphalt models for predicting thermal conductivity based on the background grid and the moving-and-densifying algorithm was proposed which greatly improves construction efficiency. The influence of the random factors on models’ stability was studied and the range of the key factors within all random factors was restricted. Further, a conflict judgment method for the convex aggregate and the improved take-and-place method based on the background grid method and the moving-and-densifying algorithm was realized by MATLAB code to construct aggregate mixture models. Finally, the aggregate mixtures model was imported into ABAQUS 2022 to predict the thermal conductivity based on the steady-state plate method, and the validity of the predicting result was verified by experimental result. With this construction method, the stability index is improved by more than 80.71%, and packing efficiency is 198.98% higher than before. Additionally, the 3D random model showed a smaller prediction error range (less than 5%) than the 2D models (more than 10%) and was more accurate than the 2D prediction model. This research focused on improving the construction efficiency of the 3D random asphalt concrete model which contributes to full utilization and laying a foundation for further improvement. Full article

(This article belongs to the Special Issue Advanced Materials and Novel Technique in Civil Engineering)

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