A hybrid machine learning model to estimate self-compacting concrete compressive strength

This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.     

混凝土裂缝宽度预测模型研究

随着各种添加剂、掺合料的混凝土不断地应用到建筑中,裂缝的出现并没有得到有效的解决。而有些在宽度允许内的微小裂缝对建筑的稳定性没有实质性的影响。如何预测裂缝的开裂趋势,可以有效地判断裂缝对建筑稳定性的影响程度。通过灰模型的滚动预测法对裂缝的数值进行预测,并从模型的内部寻找裂缝的开裂规律。将模型的预测结果和实际工程情况相结合,取出最能代表实际情况的样本数据重新建立模型。同时,将预测结果和线性回归以及神经网络的结果做出比较,取得了很好的效果,具有一定的指导意义。     

A machine learning approach based on multifractal features for crack assessment of reinforced concrete shells

The geometric properties and spatial characteristics of crack patterns are significant indicators of the extent of damage on reinforced concrete structures. However, manual visual assessment is subjective and depends highly on the inspector's skills. The current study proposes an automated approach for the quantification of digitally documented crack patterns on reinforced concrete shell elements subjected to reversed cyclic shear loading. Multifractal analysis is proposed as a feature extractor for images depicting crack patterns and a set of artificial cracks is analyzed, to quantify how the properties of crack patterns vary as a function of cracking inclination. The results of the parametric study motivated the training of a multiclass classification model, which is used to provide damage level estimates for cracked reinforced concrete members. The training of the classifier is performed using experimental data of reinforced concrete shell elements under well‐defined and idealized two‐dimensional pure shear stress loading conditions. A dataset with 119 images from crack patterns of reinforced concrete shells is used for training. The multifractal features successfully translate the shape of the crack patterns into meaningful information about the extent of damage; achieving an overall test accuracy of 89.3%.     

桥梁结构物混凝土表面裂缝病害分析

针对工程质量的重要性,结合桥梁工程的施工要点,分析了桥梁结构物混凝土表面裂缝产生的原因,并针对性地提出了有效的预防措施,从而解决桥梁结构物混凝土表面的裂缝.     

钢筋混凝土桥梁裂缝检测及预防措施

详细分析了混凝土桥梁常见裂缝的成因,在此基础上总结了混凝土裂缝检测与监测的方法,并介绍了裂缝的预防措施,以保证桥梁结构的安全性和使用寿命。     

高速铁路桥梁墩身混凝土裂缝控制技术探讨

以某高速铁路桥梁墩身施工为例,主要从外力荷载、地质条件、原材料、施工工艺等方面,对裂缝的成因进行了分析,并采取了相应的预防和控制措施,以避免高速铁路桥梁墩身混凝土裂缝的产生。     

Applicability of performance improvement method of existing steel railway bridges by installing concrete decks

Steel railway bridges which exceed their design lifetime are increasing in Japan, and some of them have problems such as corrosion and fatigue. In this study, we proposed a method to improve the performance, such as the load-carrying capacity, of the existing steel railway bridges by installation of concrete decks. To figure out the applicability of the proposed method, we discussed the applicable range of span and the effect of stress reduction. Considering application into actual bridges, we proposed an installation method using pre-cast concrete decks and girder-deck connection with filler mortar and steel fasteners. Finally, we carried out loading tests of the connection with fasteners and bending tests of the applied girders. As a result, we found that the girder-deck connections have enough static capacity for lateral force and train loads, under the condition that the studs are installed and the gaps between the pre-cast decks are filled with mortar.     

桥梁大体积混凝土裂缝产生机理及抗裂措施

深入分析了桥梁大体积混凝土施工中温度裂缝、收缩裂缝产生的机理,从原材料选择、配合比设计、施工养护及约束控制四个方面阐述了防止裂缝产生的方法和措施,以确保桥梁的正常使用及安全。     

混凝土材料微裂纹模型研究

混凝土材料中,需要添加纤维对其进行增强与增韧,通常采用的短纤维,纤维与混凝土结合处分布着很多的微裂纹,使得材料的增强效应降低。通过微观模型的建立,研究了纤维与混凝土基体材料的结合性能,这将对研究纤维材料对混凝土的增强有重要的理论价值。     

混凝土局部损伤裂缝带模型研究

为消除混凝土开裂使有限元分析结果对单元网格尺寸产生的依赖性,在基于局部本构模型的材料非线性有限元分析中,建立了混凝土局部损伤裂缝带模型。结果表明：该模型所采用的三参数幂函数型拉伸损伤演化方程,可满足不同单元网格尺寸下应力-应变关系调整的需要;在局部损伤本构模型中引入断裂能和与单元网格尺寸相关的裂缝带宽度,可在保证断裂能客观性的前提下依据初始应力-开裂应变关系推导不同单元网格尺寸下的应力-开裂应变关系;提出基于异步粒子群智能算法的优化反演方法,可实现在有限元分析中依据应力-开裂应变关系自适应,从而确定不同单元网格尺寸下的模型参数。在ABAQUS软件平台上采用所建立的模型对算例进行了数值模拟,验证了模型的有效性与程序编制的正确性。     

Local damage and crack band model for concrete

为消除混凝土开裂使有限元分析结果对单元网格尺寸产生的依赖性，在基于局部本构模型的材料非线性有限元分析中，建立了混凝土局部损伤裂缝带模型。结果表明：该模型所采用的三参数幂函数型拉伸损伤演化方程，可满足不同单元网格尺寸下应力-应变关系调整的需要；在局部损伤本构模型中引入断裂能和与单元网格尺寸相关的裂缝带宽度，可在保证断裂能客观性的前提下依据初始应力-开裂应变关系推导不同单元网格尺寸下的应力-开裂应变关系；提出基于异步粒子群智能算法的优化反演方法，可实现在有限元分析中依据应力-开裂应变关系自适应，从而确定不同单元网格尺寸下的模型参数。在ABAQUS软件平台上采用所建立的模型对算例进行了数值模拟，验证了模型的有效性与程序编制的正确性。     

基于温度-应力试验机的水工混凝土抗裂性试验方法

目前水工混凝土采用极限拉伸值和绝热温升来评判抗裂性的方法未能客观反映混凝土实际的抗裂表现。重大水利工程对混凝土的抗裂性提出了更高的要求,迫切需要一种科学、综合的试验方法来客观评价混凝土的抗裂性。作为一种先进的设备,温度-应力试验机能够克服目前混凝土抗裂性试验方法的缺陷,可以实现以下功能:测量半绝热及模拟实际坝体温度历程下混凝土力学及变形性能的发展规律、监测不同约束状态下的应力应变发展、测量混凝土的真实开裂温度以及早期拉徐变性能。因此,该试验方法能够为重大工程的混凝土配合比设计及优化提供试验依据,客观地评价不同配合比混凝土的开裂风险。     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Fibre-reinforced polymer (FRP) provides an alternative reinforcement for concrete flat slabs. This research proposes a hybrid machine learning model for predicting the ultimate punching shear capacity of FRP-reinforced slabs. The model employs the least squares support vector machine (LS-SVM) to discover the mapping between the influencing factors and the slab punching capacity. Furthermore, the firefly algorithm (FA), a population-based metaheuristic, is utilised to facilitate the LS-SVM training. A data-set which contains actual tests of FRP-reinforced concrete slabs is utilised to construct and verify the proposed approach. The contribution of this research is to establish a hybrid machine method, based on the LS-SVM and FA algorithms, for meliorating the prediction accuracy of FRP-reinforced slabs’ ultimate punching shear capacity. Experimental results demonstrate that the new model has achieved roughly 55 and 15% reductions in terms of Root Mean Squared Error compared with the formula-based and Artificial Neural Network methods, respectively.     

大体积混凝土裂缝的防治

混凝土在硬化和使用过程中,受各种因素的影响,会产生一些裂缝,尤其对大体积混凝土结构而言,防治裂缝是一个较为复杂的问题。本文分析了大体积混凝土裂缝产生的原因,提出了防止大体积混凝土施工中出现有害裂缝的措施。     

高强混凝土强度预测的高斯过程机器学习模型

高强混凝土强度与其影响因素之间存在着复杂的非线性关系,针对现有方法的局限性,建立了基于高斯过程机器学习的高强混凝土强度预测模型.该模型采用高斯过程机器学习方法对少量试验样本的测试结果进行学习,就可以建立高强混凝土强度与其影响因素之间的复杂非线性映射关系.研究结果表明:高强混凝土强度预测的高斯过程机器学习模型是可行的,具有预测精度高、适用性强、参数自适应化且易于实现的优点.     

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

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.     

Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model

Abstract

Preventive maintenance is increasingly becoming an essential strategy in the bridge industry owing to its proactive advantage of maintaining the structural sustainability during its entire service life. Several in-use bridges lack an appropriate regular maintenance solution, leading to extra cost during the operation stage. This paper proposes a new generation of the bridge maintenance system by using a digital twin model concept for more reliable decision-making. A detailed solution is proposed in this work to enhance the bridge maintenance process using a parallel solution: a maintenance information management system based on a 3D information model in conjunction with a digital inspection system using image processing. Three-dimensional digital models are required to utilise information from the entire lifecycle of a project, including design and construction, operation, and maintenance, by continuously exchanging and updating data from each stakeholder. For the maintenance of prestressed concrete bridges, the twin models are defined and their uses are presented.     

基于尺寸效应的混凝土等效裂纹断裂模型

根据混凝土材料断裂过程区存在尺寸效应这一特点，对混凝土等效裂纹断裂模型做了一定的研究，得到了与试件尺寸相关的等效裂纹断裂模型。使之更符合断裂过程区存在尺寸效应的特点。用该模型对已有试验资料进行分析。得出此模型在采用单直线软化模型时较为准确，采用双直线模型则相差较远的结论。     

混凝土的裂缝分析

针对混凝土工程中的裂缝问题 ,从温度裂缝、干缩裂缝、塑性收缩裂缝和沉降收缩裂缝等方面 ,就其产生的原因进行了分析 ,提出了减少裂缝产生的有效防治措施