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2021全球工程前沿 1

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三维人脸重建；级联回归；形状空间；实时 1

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

《结构与土木工程前沿（英文）》 2019年第13卷第3期页码 674-685 doi: 10.1007/s11709-018-0505-3

摘要： M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.

关键词： batter piles oblique load test neural network M5 model tree random forest regression ANOVA

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

《结构与土木工程前沿（英文）》 2020年第14卷第1期页码 185-198 doi: 10.1007/s11709-019-0591-x

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength linear regression random forest gradient boosting machine learning standard penetration test

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Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

《机械工程前沿（英文）》 2015年第10卷第3期页码 277-286 doi: 10.1007/s11465-015-0348-8

摘要：

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters’ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

关键词： fault diagnosis spur gearbox wavelet packet decomposition random forest

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

《结构与土木工程前沿（英文）》 2022年第16卷第2期页码 224-238 doi: 10.1007/s11709-022-0812-6

摘要： The consolidation coefficient of soil (C_v) is a crucial parameter used for the design of structures leaned on soft soi. In general, the C_v is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine C_v via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the C_v of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R² > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R² = 0.9869), the difference compared with other models was negligible (i.e., R² = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict C_v based on the basic soil parameters.

关键词： soil consolidation coefficient machine learning random forest Relief

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基于随机森林模型的滑动轨迹人机识别 Research Articles

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

《信息与电子工程前沿（英文）》 2019年第20卷第7期页码 925-929 doi: 10.1631/FITEE.1700442

摘要：识别码在维护网络安全的人机身份验证中得到广泛应用。人机身份验证面临的挑战包括对人与机器滑动轨迹的正确检测。提出一种基于滑动轨迹数据集的人机识别随机森林模型。通过多维性能评价指标，包括识别准确率、识别召回率、识别误报率、识别漏报率、F值和加权准确率，验证该随机森林模型以及基准模型（逻辑回归模型和支持向量机）。随机森林模型多维性能评价指标优于基准模型。

关键词：人机识别；随机森林；支持向量机；逻辑回归；多维性能评价指标

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

《结构与土木工程前沿（英文）》 2020年第14卷第5期页码 1083-1096 doi: 10.1007/s11709-020-0654-z

摘要： The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of , , and . Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria ( =0.841, =0.592, and =0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure flexible pavement structural number prediction Gaussian process regression M5P model tree random forest

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of catalyst temperature in automotive engines over coldstart operation in the presence of different randomnoises and uncertainty: Implementation of generalized Gaussian process regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

《机械工程前沿（英文）》 2015年第10卷第4期页码 405-412 doi: 10.1007/s11465-015-0354-x

摘要：

The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines’ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system’s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system’s behavior over coldstart operations. Then, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of analysis of the engine’s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.

关键词： automotive engine calibration coldstart operation Gaussian process regression machine (GPRM) uncertainty and random noises

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Progress of forest certification in China

Wenming LU, Maharaj MUTHOO

《农业科学与工程前沿（英文）》 2017年第4卷第4期页码 414-420 doi: 10.15302/J-FASE-2017185

摘要： The Chinese Government is committed to forest certification as a market-based instrument to promote sustainable forest management. Forest certification includes a number of regulations, rules and policy paradigms related to certification and there are numerous challenges facing the uptake of forest certification in China. In particular, the ban on commercial logging in natural forests implemented by the Natural Forest Protection Program has resulted in little demand for forest certification of natural forest management units. However, there are also certification opportunities, such as the certification of non-timber forest products that benefit local communities who depend on forests for the goods and services they provide. This paper provides an overview of progress in forest certification in China, including its development history, organizational structure, scheme documents, certification scopes and standards, accreditation, certification bodies and auditors, and certification logos. The paper also focuses on government support for the supervision and management of forest certification through policy incentives, including the potential government procurement and subsidy policies for certified forest products. Finally, the paper analyzes certified non-timber forest products as an example of the value of certification to promote sustainable forest management and how the concept of forest certification can be used to add value to forests and ensure they are responsibly and sustainably managed. In general, forest certification in China has a clear role in sustainable forest management, both for timber and non-timber forest products.

关键词： China Forest Certification Scheme forest certification government support opportunities and challenges sustainable forest management

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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

《结构与土木工程前沿（英文）》 2022年第16卷第9期页码 1153-1169 doi: 10.1007/s11709-022-0830-4

摘要： The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R² ≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

关键词： gradient boosting random forest chloride content concrete sensitivity analysis.

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

《结构与土木工程前沿（英文）》 2022年第16卷第6期页码 667-684 doi: 10.1007/s11709-022-0822-4

摘要： The study proposes a framework combining machine learning (ML) models into a logical hierarchical system which evaluates the stability of the sheet wall before other predictions. The study uses the hardening soil (HS) model to develop a 200-sample finite element analysis (FEA) database, to develop the ML models. Consequently, a system containing three trained ML models is proposed to first predict the stability status (random forest classification, RFC) followed by 1) the cantilever top horizontal displacement of sheet wall (artificial neural network regression models, RANN1) and 2) vertical settlement of soil (RANN2). The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, based on the application of random sampling technique in the data splitting process. Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 value. This implies a high performance of RFC on the database with medians of accuracy, precision, and recall, on test set are 1.0000, 1.0000, and 0.92857, respectively. The regression ANN models have coefficient of determinations on test set, as high as 0.9521 for RANN1, and 0.9988 for RANN2, respectively. The parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

关键词： finite element analysis cantilever sheet wall machine learning artificial neural network random forest

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裂缝性储层数据驱动模型证伪与不确定性量化 Article

方军龄, 龚斌, Jef Caers

《工程（英文）》 2022年第18卷第11期页码 116-128 doi: 10.1016/j.eng.2022.04.015

摘要：

天然裂缝的许多特性是不确定的，如裂缝的空间分布、岩石物理特性和流体流动性能。贝叶斯定理提供了一个框架来量化地质建模和流动模拟的不确定性，从而支持储层物性预测。贝叶斯方法在裂缝性储层中的应用大多局限于合成案例。然而，在现场应用中，一个主要问题是贝叶斯先验是被证伪的，因为它不能预测油气藏的生产历史。在本文中，我们展示了如何利用全局敏感性分析（GSA）来确定先验被证伪的原因。然后，我们采用近似贝叶斯计算（ABC）方法，结合基于决策树的代理模型来拟合生产历史。我们将这两种方法应用于一个复杂的裂缝性油气藏，其中综合考虑了所有不确定因素，包括油层物理特性、岩石物理特性、流体特性、离散裂缝参数以及压力和渗透率的动态变化。我们成功地找出了证伪的几个原因。结果表明，我们提出的方法可以有效地量化裂缝性储层建模和流动模拟的不确定性。此外，关键参数的不确定性，如裂缝开度和断层传导率，得到了降低。

关键词：贝叶斯证据学习证伪裂隙性储层随机森林近似贝叶斯计算

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森林火灾预报的新视角

赵宪文

《中国工程科学》 2000年第2卷第5期页码 66-71

摘要：

从森林火灾成灾的三个基本因子（火源、环境和可燃物）入手，在宏观上对西南地区森林火灾预报方法进行了探讨。在预测林火发生方面，考虑到人为火是发生森林火灾的主要原因，有很大随机性，因而采用了马尔科夫随机过程理论。在分析成灾环境时，采用了与天、地、生相关分析的方法。特别在可燃物的贮量估测方面，用航天遥感数据，提出一套新的框算方法，为在区域的尺度上估测森林火灾给出了有效的、定量的方法，从而提高了可信度。

关键词：森林火灾预报航天遥感火灾基本因子新视角

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我国林产化学工业发展的新动向

宋湛谦

《中国工程科学》 2001年第3卷第2期页码 1-6

摘要：

林产化学工业是将可再生的森林资源经过化学加工生产出各种有用的产品。它是森林资源高效可持续利用的一个重要组成部分。文章介绍我国林产化学工业的现状，并指出今后发展方向，即加强创新研究，开发深加工产品；推进林产化工企业向大型化发展；发展木材制浆造纸和开发木质能源。

关键词：林产化学工业森林资源化学加工

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Cattle manure biochar and earthworm interactively affected CO and NO emissions in agricultural and forest

《环境科学与工程前沿（英文）》 2022年第16卷第3期 doi: 10.1007/s11783-021-1473-8

摘要：

• Earthworms increase CO₂ and N₂O emissions in agricultural and forest soil.

关键词： Carbon sequestration Forest soil Cattle manure biochar Greenhouse gas emissions Soil fauna

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Floating forest: A novel breakwater-windbreak structure against wind and wave hazards

《结构与土木工程前沿（英文）》 2021年第15卷第5期页码 1111-1127 doi: 10.1007/s11709-021-0757-1

摘要： A novel floating breakwater-windbreak structure (floating forest) has been designed for the protection of vulnerable coastal areas from extreme wind and wave loadings during storm conditions. The modular arch-shaped concrete structure is positioned perpendicularly to the direction of the prevailing wave and wind. The structure below the water surface acts as a porous breakwater with wave scattering capability. An array of tubular columns on the sloping deck of the breakwater act as an artificial forest-type windbreak. A feasibility study involving hydrodynamic and aerodynamic analyses has been performed, focusing on its capability in reducing wave heights and wind speeds in the lee side. The study shows that the proposed 1 km long floating forest is able to shelter a lee area that stretches up to 600 m, with 40%–60% wave energy reduction and 10%–80% peak wind speed reduction.

关键词： floating structure breakwater windbreak hydrodynamic CFD

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标题作者时间类型操作

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

期刊论文

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

期刊论文

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

期刊论文

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

期刊论文

基于随机森林模型的滑动轨迹人机识别

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

期刊论文

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

期刊论文

of catalyst temperature in automotive engines over coldstart operation in the presence of different randomnoises and uncertainty: Implementation of generalized Gaussian process regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

期刊论文

Progress of forest certification in China

Wenming LU, Maharaj MUTHOO

期刊论文

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

期刊论文

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

期刊论文

裂缝性储层数据驱动模型证伪与不确定性量化