• Smart Structures and Systems
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• v.33 no.5
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• Advances in concrete construction
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• v.17 no.1
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• pp.13-26
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• 2024

Due to its capacity to address urgent environmental challenges connected to urbanization and stormwater management, pervious concrete, a sustainable and innovative material, has attracted a lot of attention recently. The aim of this study was to find the engineering characteristics of pervious concrete made from recycled aggregate (RA) at various aggregate-to-cement ratios (A/C) and the addition of 5% (by weight of total aggregate) of both natural and recycled fine aggregate to produce a very sustainable concrete product for a variety of applications. The three distinct aggregate-to-cement ratios, 6, 5, and 4, were used to produce pervious concrete using recycled aggregate in the research approach. The ratio of water to cement (w/c) was maintained at 0.3. Pervious concrete was created using single-sized recycled aggregate that passed through a 12.5 mm sieve and was held on a 9.5 mm sieve, as well as natural and recycled sand that passed through a 4 mm sieve. The production of twelve distinct concrete mixtures resulted in the testing of each concrete sample for dry density, abrasion resistance, compressive and splitting tensile strengths, porosity, and water permeability. A statistical method called GLM-ANOVA was also used to assess the characteristics of pervious concrete made using recycled aggregate. According to the experimental results, lowering the aggregate-to-cement ratio enhances the pervious concrete's overall performance. Additionally, a modest amount of fine aggregate boosts mechanical strength while lowering void content and water permeability. However, it was noted that such concretes' mechanical qualities were adversely affected to some extent. The results of this study offer insight into the viability of using recycled aggregates in order to achieve both structural integrity and environmental friendliness, which helps to optimize pervious concrete compositions.

• Advances in concrete construction
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• v.17 no.2
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• pp.93-110
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• 2024

The process of concrete production consumes an immense volume of water, with approximately one billion metric tons of freshwater being utilized for tasks such as aggregate washing, fresh concrete production, and concrete curing. The accessibility of clean water for the public is hindered by the limited availability of water resources, primarily due to the rapid expansion of industries such as tanneries, stone quarries, and concrete manufacturing. These industries not only consume substantial amounts of freshwater but also generate significant volumes of various types of waste. Therefore, the use of fresh water in concrete production should be minimized. Few studies have reviewed the production of concrete using wastewater to derive practical and applicable findings for the industry. Thus, this study thoroughly explores the physical and chemical effects of wastewater on concrete, examining aspects like durability, hardened properties, and rheological characteristics. It identifies key factors that can compromise concrete properties when exposed to wastewater. The scarcity of research on integrating wastewater into concrete production underscores the urgent necessity for innovative approaches and methodologies in this field. While the inclusion of wash water typically reduces the workability of fresh concrete, it often enhances its compressive strength. Notably, significant improvements have been observed when using tertiary processed wastewater, wash water, polyvinyl alcohol-based wash water (PVAW), and reclaimed water in the concrete mixing process. The application of tertiary treatment to wastewater resulted in a notable enhancement of compressive strength, showing increases of up to 7%. In contrast, wastewater treated through secondary methods experienced a decline in strength ranging from 9% to 18% over a period of six months. However, the use of reclaimed wastewater demonstrated an improvement in strength by 8% to 17%, depending on the concentration level ranging from 25% to 100%. In contrast, the utilization of secondary processed wastewater and industrial water has a minimal impact on the concrete's strength.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Computers and Concrete
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• v.34 no.3
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• pp.279-296
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• 2024

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.247-262
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• 2024

Domestic sewage can greatly affect the macro-micro physical-mechanical properties of building foundation soils. In order to investigate the effect of domestic sewage on physical and mechanical properties of soils, the physicochemical properties of three groups of different concentrations of domestic sewage contaminated soil were tested through indoor experiments. Combined with scanning electron microscopy, X-ray diffraction experiments, and grey relational analysis, the degree of influence of different concentrations of domestic sewage on the physicochemical properties of soil was compared and analyzed from multiple perspectives such as microstructure and mineral composition, revealing the influencing mechanism of soil pollution by domestic sewage. The results showed that under the immersion of contaminated water, the color of the soaking water turned black first and then yellow, and brownish yellow secretions appeared on the surface of the soil samples. The moisture content, specific gravity, density, and pore ratio index of the soil samples immersed in 50% and 100% domestic sewage decreased with the increase of sewage concentration, while the liquid limit of the soil samples changed in the opposite direction. The immersion time had little effect on the slope of the compression curve of the soil samples soaked in tap water. For the soil samples immersed in domestic sewage, the slope of the compression curve and the compression coefficient increased with the increase of domestic sewage concentration and immersion time, while the compression modulus showed the opposite trend. In the soil samples immersed in tap water, there were a large number of small particles and cementitious substances, and the structure was relatively dense. With the increase of domestic sewage concentration, the microstructure of the soil changed significantly, with the appearance of sigle particle structure, loose and disorderly arrangement of particles, increased and enlarged pores, gradual reduction of small particle substances and cementitious substances, and the soil structure transformed from compact to loose. The research findings can provide theoretical reference for contaminated geotechnical engineering.

• Smart Structures and Systems
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• v.34 no.1
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• pp.25-32
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• 2024

As materials cure, the internal electrical flow changes, leading to variations in the dielectric constant over time. This study aims to assess the impact of voltage values extracted from time domain reflectometry (TDR) waveforms, measured during the curing of materials, on predicting the dielectric constant. The experiments are conducted over a curing period ranging from 60 to 8640 minutes, with 30 TDR trials. From the measured waveforms, values of V₀, V₁, V₂, V_f, and Δt are deduced. Additionally, curing time is included as an input variable. Groups A and B are distinguished based on the presence or absence of Δt, indicating a physical relationship between Δt and the dielectric constant. The dielectric constant is set as the output variable. The SHapley Additive exPlanations (SHAP) algorithm is applied to the compiled data. The results indicate that Δt and V₁ are the most influential input variables in both Group-A and Group-B. The study also presents the distribution of SHAP values and interacts SHAP values to infer the interrelationships among the input variables. To validate the reliability of these findings, the partial dependence (PD) algorithm is applied to estimate the marginal effects of each input variable, with outcomes closely aligning with those of the SHAP algorithm. This research suggests that understanding the contributions and proportional relationships of each input variable can aid in interpreting the relationships among various material properties.

• Smart Structures and Systems
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• v.34 no.1
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• pp.33-40
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• 2024

We aimed to assess the evolution of small-strain stiffness and relative density in non-compacted embankment layers. We developed embedded type in-situ soil stiffness measurement devices for monitoring small-strain stiffness occurring after filling at a test site and conducted comprehensive laboratory compaction tests using an oedometer cell with a bender element. However, direct comparison is extremely difficult because the shear wave velocity measured in the field and laboratory depend on depth and effective stress, respectively. Therefore, we propose a method for establishing a relationship between effective stress and depth using a compressibility model. In this study, the shear wave velocity measured in the field was compared to the estimated shear wave velocity-depth profiles for completely dry and saturated conditions with different relative densities. The relative density under saturated soil conditions may vary between 50% and 90% and tends to be closer to 95%. Under dry soil conditions, the relative density of the embankment can vary from 30% to 70% and tends to approach 76%. For model validation, the relative density estimated from shear wave velocity-depth profiles was compared to that estimated from DCPI data. In other words, the results analyzed in the context of an effective stress-depth model enable the prediction of engineering properties such as the small-strain stiffness and relative density of embankment layers. This study demonstrates that physics-based data analyses successfully capture the relative density of non-compacted embankment layers.

• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.173-185
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• 2024

Currently, the electro-hydraulic servo loading control system manufactured by MTS, OpenFresco hybrid test interface software and OpenSees finite element software are widely used in structure laboratories to carry out hybrid test, but there is no relevant public information about full structure pseudo-dynamic test based on the above software and hardware. In order to study the feasibility of using the above software and hardware to carry out full structure pseudo-dynamic test, the full structure pseudo-dynamic virtual experiments of a single degree of freedom (SDOF) structure and a two degrees of freedom (2DOFs) structure are carried out based on the MTS 793 Demo Mode, and the results are respectively compared with the finite element analysis method. The results show that the finite element analysis results and full structure pseudo-dynamic virtual experiment results are highly consistent, which verifies the feasibility of carrying out the full structure pseudo-dynamic test based on the above software and hardware. Then, a three story steel frame full structure pseudo-dynamic test is conducted, and the smooth implementation of full structure pseudo-dynamic test of the three story steel frame further verifies the reliability of thistesting method. The implementation method of carrying out the full structure pseudo-dynamic tests are introduced in detail, which can provide some reference for relevant research.

• Structural Monitoring and Maintenance
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• v.11 no.3
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• pp.187-202
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• 2024

For the problems of high compressibility and low strength of peat soil formed by lake-phase deposition in Dianchi Lake, microbial-induced calcium carbonate deposition (MICP), phyto-urease-induced calcium carbonate deposition (EICP) and phyto-urease-induced calcium carbonate deposition combined with lignin (EICP combined with lignin) were used to reinforce the peat soil, the changes in mechanical properties of the soil before and after the reinforcement of the peat soil were experimentally investigated, and the effect and mechanism of peat soil reinforcing by the three reinforcing techniques were tested and analyzed using X-ray diffraction (XRD) and scanning electron microscope (SEM). The results show that: compared to the unreinforced remolded peat soil specimens, the unconfined compressive strength (UCS), cohesion and internal friction angle of the specimens reinforced by MICP, EICP and EICP combined with lignin techniques have been greatly improved, and the permeability resistance has been improved by two, two and three orders of magnitude, respectively; the different methods of reinforcing generate different calcium carbonate crystalline phases, with the EICP combined with lignin technique generating the most stable calcite, and the MICP and EICP techniques generating a mixed phase of calcite and spherulitic chalcocite. Analyses showed that for peat soil reinforcement, the acidic environment of peat soil inhibited the growth and reproduction of bacteria, EICP technology was superior to MICP technology, and the addition of lignin solved the defect of the EICP technology that did not have a "nucleation site", so EICP combined with lignin reinforcement was preferred for the improvement of peat soil.