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Journal of Rock Mechanics and Geotechnical Engineering 2020年第1期12卷 180-187页

作者： Aswin Lim Petra Cahaya Atmaja Siska Rustiani department of civil Engineering Universitas Katolik ParahyanganBandung40141Indonesia

This article presents an innovative method of bio-mediated soil improvement for increasing the shear strength of loose sand.The improvement is realized by mixing the loose sand with the inoculum of Rhizopus oligosporus,a kind of fungus widely used in food industry for making Indonesian tempeh.The objective of this article is to investigate the performance and mechanism of mixing tempeh inoculum as a binding agent of loose sand particles.The inoculum dosage,water content of loose sand,and curing time were examined for identifying the increment of unconfined compressive strength(q_u)of the samples.The results showed that q_u of the treated samples increased when the inoculum dosage was elevated.It shows that 5.24%inoculum could yield 68 kPa of q_u,and 5%water content and 3 d curing time produced the maximum q_u.Moreover,the mechanism of hypha and mycelium in binding the soil particles was clearly observed using a digital microscope and scanning electron microscope.

关键词： Bio-mediated soil improvement Loose sand Rhizopus oligosporus

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变权综合评价法在山区道路安全评价中应用

系统工程 2010年第5期28卷 85-88页

作者：游克思孙璐顾文钧东南大学交通学院江苏南京210096 department of civil Engineering Catholic University of America

通过对山区道路交通事故资料调查分析,研究了道路条件对行车安全影响,在此基础上从四个方面建立了相应的评价指标体系。将变权原理引入模糊综合评价,采用惩罚型变权模式,建立了山区道路安全评价变权综合评价模型。通过实例应用,分别采... 详细信息

通过对山区道路交通事故资料调查分析,研究了道路条件对行车安全影响,在此基础上从四个方面建立了相应的评价指标体系。将变权原理引入模糊综合评价,采用惩罚型变权模式,建立了山区道路安全评价变权综合评价模型。通过实例应用,分别采用常权和变权两种方法计算,结果表明,采用变权模糊综合评价法得到的结果更容易区分评价等级,能够突现出部分指标因素在较差情况下时对道路整体安全水平的影响,评价结果更接近实际。

关键词：山区道路安全变权综合评价

Developing a Lagrangian sediment transport model for open channel flows

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International Journal of Sediment Research 2023年第2期38卷 153-165页

作者： Saman Baharvand Habib Ahmari Poorya Taghvaei department of civil Engineering The University of Texas at Arlington department of civil Engineering Semnan University

A three-dimensional stochastic Lagrangian particle tracking sediment transport model is developed to solve the discrete advection-dispersion equation using a combination of empirical dispersion equations.The performance of three widely-used longitudinal dispersion coefficient equations was examined to select one of them as the primary dispersion equation term in the developed model. Also, a conditional empirical equation was used to consider the effect of vertical dispersion term in top layers near the water surface. The performance of particle tracking model(PTM) to calculate the sediment concentration was evaluated for various sediment classes(very fine sand, fine sand, and medium sand) using available laboratory dataset. Multiple statistical measures were calculated using the Taylor diagram for each dispersion equation. Based on the result, the developed particle tracking model estimated the suspended sediment concentration in a rectangular open channel with a correlation coefficient(R) of 0.96, standard deviation(STD) of 0.262, and root mean square deviation(RMSD) of 0.06 for three different sediment classes proving the acceptable accuracy of the developed model for different range of sediment gradations. In addition, the accuracy of PTM was compared to recently developed models. Comparison between the particle tracking model and the analytical solution of the advection-dispersion equation showed that the developed model predicted the maximum concentration of suspended sediment 6% and9.4% lower than the analytical solution result for 30 and 50 s simulation time in the straight channel,respectively. However, there was a good agreement between the longitudinal and transverse sediment concentration distribution from the model and the analytical solution approach. The result of the sensitivity analysis and validation process showed that the model could be used to simulate sediment transport in open channel flows.

关键词： Sediment transport Lagrangian particle tracking Stochastic approach Advection–dispersion Random walk

Exploring the potential of artificial intelligence techniques in prediction of the removal efficiency of vortex tube silt ejector

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International Journal of Sediment Research 2023年第4期38卷 615-627页

作者： Sanjeev Kumar Chandra Shekhar Prasad Ojha Nand Kumar Tiwari Subodh Ranjan department of civil Engineering Indian Institute of Technology department of civil Engineering National Institute of Technology

A vortex tube silt ejector is a curative hydraulic structure used to remove sediment deposits from canals and is recognized as one of the most efficient substitutes for physically removing canal sediment. The spatially varied flow in the channel and the rotational flow behavior in the tube make the silt removal process complex. It is even harder to accurately predict the silt removal efficiency by traditional models accurately. However, artificial intelligence(AI) and machine learning approaches have emerged as robust substitutes for studying complex processes. Therefore, this research makes use of AI approaches; support vector machine(SVM), random forest(RF), random tree(RT), and multivariate adaptive regression spline(MARS) to compute the vortex tube silt ejection efficiency using the laboratory data sets. The outcomes of the artificial intelligence(AI)-based techniques also were compared with traditional models. It was found that the RT model(root mean square error, RMSE = 2.165, Nash Sutcliffe efficiency, NSE = 0.98) outperforms the other applied approaches which had relatively more significant result errors. The sensitivity analysis of the process depicts the extraction ratio as the key parameter in the computation of vortex tube silt ejector removal efficiency. The findings of the AI-based approaches discussed in the current study might be helpful for hydraulic engineers as well as researchers in the assessment of the removal efficiency of vortex tube silt ejectors.

关键词： Vortex tube silt ejector Support vector machine Random forest Random tree Multivariate adaptive regression spline

Experimental investigation of sediment transport in partially ice-covered channels

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International Journal of Sediment Research 2023年第5期38卷 769-779页

作者： Mina Rouzegar Shawn P.Clark department of civil Engineering University of Manitoba

It is important to understand the effects of ice cover on sediment transport in cold climates, where subfreezing temperatures affect water bodies for a significant part of the year. The literature contains many studies on sediment transport in open channel flow, and several studies on sediment transport in completely ice-covered flow. There has been little or no research on sediment transport in partially icecovered channels. In the current study, laboratory experiments were done in a rectangular flume to quantify the impact of border ice presence on the sediment transport rate. The effects of ice cover extent and changing flow strengths on sediment transport distribution also were investigated, and the results were compared to those for fully ice-covered and open channel flow. The ice coverage ratios considered were 0(representing the open water condition), 0.25, 0.50, 0.67, and 1(representing fully ice-covered flow). The partial ice cover was found to impact the sediment transport distribution within the channel. The effect of ice coverage extent on sediment transport distribution was more significant at lower flow strengths and became negligible at higher flow strengths. The conventional equations for sediment transport in open channel flow and fully ice-covered flow that relate the dimensionless bedload transport rate to the flow strength were found to be applicable to estimate the total cross-section-averaged bedload transport for partially ice-covered flow when modified appropriately. Empirical coefficients for these equations were determined using the experimental data.

关键词： Sediment transport Bedload Ice-covered channel Border ice

Steel rings as seismic fuses for enhancing ductility of cross braced frames

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Earthquake Engineering and Engineering Vibration 2022年第4期21卷 1103-1117页

作者： Farhad Behnamfar Saeid Arman Hassan Zibasokhan department of civil Engineering Isfahan University of TechnologyIsfahan ***Iran department of civil Engineering Kermanshah University of TechnologyKermanshah ***Iran

A new remedy is proposed in this study to increase the ductility of cross-braced frames to a level comparable with ductile moment frames.The suggested system consists of one or two concentric steel rings installed in the cross-braced bay vertically.The steel rings are designed such that they fail in bending sooner than failure of the braces in compression.Then the rings act as seismic fuses with multiple bending plastic hinges.Using nonlinear static analysis,it is shown that the proposed system can be designed to behave like cross-braced frames with regard to stiffness and strength,and like special moment frames with regard to ductility.Seismic design factors for the proposed system are recommended based on nonlinear pushover and cyclic analysis studies.

关键词： cross brace special moment frame steel ring seismic fuse ductility

Predicting vehicle fuel consumption patterns using floating vehicle data

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Journal of Environmental Sciences 2017年第9期29卷 24-29页

作者： Yiman Du Jianping Wu Senyan Yang Liutong Zhou department of civil Engineering Tsinghua University department of civil Engineering Columbia University

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.

关键词： Vehicle fuel consumption Prediction Floating vehicle data

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

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Earthquake Engineering and Engineering Vibration 2023年第1期22卷 41-54页

作者： Shun-Hsiang Hsu Ho-Tin Hung Yu-Qi Lin Chia-Ming Chang department of civil and Environmental Engineering University of Illinois at Urbana-ChampaignUSA department of civil Engineering Taiwan University(NTU)TaiwanChina

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.

关键词： visual inspection damage detection augmented reality damage quantification deep learning

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

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Frontiers of Structural and civil Engineering 2022年第1期16卷 57-74页

作者： Serdar CARBAS Musa ARTAR department of civil Engineering Karamanoglu Mehmetbey UniversityKaraman 70200Turkey department of civil Engineering Bayburt UniversityBayburt 69000Turkey

Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.

关键词： steel dome optimization water strider algorithm grey wolf algorithm brain storm optimization algorithm hyperband optimization algorithm

Detection of critical road roughness sections by trend analysis and investigation of driver speed interaction

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Frontiers of Structural and civil Engineering 2022年第4期16卷 515-532页

作者： Meltem SAPLIOGLU Ayse UNAL Melek BOCEK department of civil Engineering Suleyman Demirel UniversityIsparta 32260Turkey department of civil Engineering Siirt UniversitySiirt 56100Turkey

Pavement roughness(IRI—International Roughness Index values)influence the stability of traffic movements both on intercity roads and urban roads.This study is to determine the exact locations of critical pavement roughness values that affect traffic motion stability and comfort in city centre highway arteries.Roughness data with 10 m intervals were collected on a 3140 m divided road containing three consecutive signalized intersections in the city centre arterial.These data were analysed using the distance-dependent Mann-Kendall trend analysis method and checkerboard model.The sections where roughness is important were determined at a 95%confidence interval.The results will show where future pavement improvements should be prioritized for municipalities and road maintenance engineers and will form a basis for the urban road management system.

关键词： trend analysis checkerboard model IRI driver speed