Stochastic lattice discrete particle modeling of fracture in pervious concrete

Cementitious coating effectively prevents chloride‐induced corrosion in offshore reinforced concrete (RC) structures. Strain hardening cementitious composites (SHCC) is a class of advanced cementitious composites with superior ductility and durability. Due to the lack of long‐term test data, the current probability‐based design guidelines may not be applicable to SHCC coating. This paper develops a fuzzy‐stochastic–based approach to assess the durability performance of coatings using cement mortar and engineered cementitious composites (ECC), a specific type of SHCC. The age factor and the mean of chloride diffusion coefficient are modeled as fuzzy variables, and the chloride diffusion coefficient follows Gaussian distribution. Based on limited test data and subjective opinion, membership functions are proposed for the mean of initial chloride diffusion coefficient and the age factor of cement and ECC. To achieve equivalent durability performance of cement mortar coating, the amount of uncracked and cracked ECC coating are estimated to be around 33% and 25%, respectively. Based on equivalent durability performance, life cycle assessment (LCA) is conducted to compare the ecological performance of the two coatings. A case study, based on a 25‐m‐long simply supported box girder in Shenzhen Bay, is given to elaborate the proposed approach and the evaluation results of durability performance and ecological impacts.

Section: Fuzzy Variablementioning

confidence: 99%

Predicting the life cycle durability and ecological performance of cementitious coatings with a fuzzy‐stochastics–based approach

Huang

Zhou

et al. 2023

“…In recent years, with the development of non‐contact computer vision technology, many research teams have tried to combine it with intelligent algorithms to focus on problems in civil engineering (Ahmadkhanlou & Adeli, 2005; Aldwaik & Adeli, 2016; Fascetti et al., 2022). Some studies proposed methods of accurate surface assessment to replace the conventional inspection or apply to inaccessible surfaces (Xie et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Automated flatness assessment for large quantities of full‐scale precast beams using laser scanning

Xu,

Xiong,

Tang

et al. 2024

Prefabrication has been widely used in bridge construction, for which precast beams are produced from a beam yard and constructed with a cast‐in‐suit bridge deck. The developments recently are focusing on large dimensions or large quantities of beam units, which leads to the inevitable challenge of beam quality control. Among them, beam surface flatness as one of the important indicators for manufacturing quality needs to be strictly controlled to ensure structural performance. However, the commonly used methods still rely heavily on manually contact‐based inspection, which is not only time‐consuming but also error‐prone. This paper presents an automated flatness assessment for large quantities of full‐scale precast beams by using terrestrial laser scanning techniques. Through axis calibration, target surface segmentation, and flatness deviation calculation, flatness deviations of the segmented surfaces from the point cloud of the entire beam are calculated, and colored maps are generated to visually assess the flatness quality. The proposed approach was applied to numerical simulated models as well as laboratory‐scale specimens for accuracy validation. Besides, its processing time was also compared to conventional contact‐based inspection during a field test. Moreover, efforts are devoted to applying the proposed method on nearly a thousand precast beams in an in‐field bridge construction project. Results can demonstrate its outperformance not only on accuracy but also the efficiency and practicality when applied to large quantities of full‐scale precast beams.

“…X‐ray computed tomography (XCT) images and Monte Carlo scene generation methods are commonly used to model the concrete microstructure, and the latter is now extensively applied to representing the configuration of concrete microstructures due to its lower implementation cost (Xu et al., 2015). Meanwhile, many micromechanical models such as the lattice model (LM), random particle model (RPM) and random aggregate model (RAM) have been proposed to investigate the physical behavior of concrete (Du et al., 2022; Fascetti et al., 2022). Generally accepted, LM and RPM can more accurately and effectively simulate the development process of various initial defects and cracks in materials.…”

Section: Introductionmentioning

confidence: 99%

Hybrid random aggregation model and Bayesian optimization‐based convolutional neural network for estimating the concrete compressive strength

Li,

Pan,

Guo

et al. 2023

Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two‐dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate and other concrete ingredients were represented innovatively using polygons and trichromatic chromaticity values in the RAM images. The RAM image set was created by applying this method to represent 1110 sets of different concrete mixes. Then based on the Bayesian optimization algorithm and the image set, a compressive strength prediction model considering the effect of coarse aggregate types and gradations was developed utilizing a convolutional neural network (CNN) model. Meanwhile, an artificial neural network (ANN) compressive strength prediction model was developed using 1110 sets of mix ratio data. The results show that the proposed RAM image generation method has the capability to represent different concrete mix ratios collected in this study. The prediction performance of the CNN compressive strength model considering aggregate types and gradations is better than that of the ANN model. The method can provide a new perspective for predicting other concrete mechanical properties and technically support performance‐based intelligent concrete mix design.