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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

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Frontiers of Structural and civil engineering 2023年第2期17卷 284-305页

作者： Hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH Hai-Bang LY Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed *** study developed different ML models for predicting the CS of FRSCC to address these *** neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model *** findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of *** results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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An integrated decision-making approach to resilience–LCC Bridge network retrofitting using a genetic algorithm-based framework

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Resilient Cities and Structures 2025年第1期4卷 16-40页

作者： Omidian, Pedram Khaji, Naser Aghakouchak, Ali Akbar Faculty of Civil and Environmental Engineering Tarbiat Modares University Tehran P.O. Box 14115-397 Iran Civil and Environmental Engineering Program of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8527 Japan

Bridge networks are essential components of civil infrastructure, supporting communities by delivering vital services and facilitating economic activities. However, bridges are vulnerable to natural disasters, particularly earthquakes. To develop an effective disaster management strategy, it is critical to identify reliable, robust, and efficient indicators. In this regard, Life-Cycle Cost (LCC) and Resilience (R) serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans. This study proposes an innovative LCC–R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan. The proposed framework employs both single- and multi-objective optimization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks. The considered retrofit strategies include various options such as different materials (steel, CFRP, and GFRP), thicknesses, arrangements, and timing of retrofitting actions. The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incremental dynamic analyses for each case. In the subsequent step, the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function. Next, the LCC is evaluated according to the proposed formulation for multiple seismic occurrences, which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events. For optimization purposes, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions. The study presents the most effective retrofit strategies for an illustrative bridge network, providing a comprehensive discussion and insights into the resulting tactical approaches

关键词： Genetic algorithms

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Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water

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Frontiers of environmental Science & engineering 2023年第5期17卷 51-65页

作者： Seo Won Cho Haoran Wei Environmental Chemistry and Technology Program University of Wisconsin–MadisonMadisonWI 53706USA Department of Civil and Environmental Engineering University of Wisconsin–MadisonMadisonWI 53706USA

Emerging contaminants(ECs)in drinking water pose threats to public health due to their environmental prevalence and potential *** occurrence of ECs in our drinking water supplies depends on their physicochemical properties,discharging rate,and susceptibility to removal by water treatment *** health effects of long-term exposure to ECs justify their regular monitoring in drinking water *** this review article,we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy(SERS)for EC analysis in drinking *** principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost,time,sensitivity,and availability is ***,we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per-and polyfluoroalkyl substances,novel pesticides,pharmaceuticals,endocrine-disrupting chemicals,and *** addition to maximizing the intrinsic enhancement factors of SERS substrates,strategies to improve hot spot accessibilities to the targeting ECs are equally *** is a review article focusing on SERS analysis of ECs in drinking *** discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.

关键词： Emerging contaminant Surface-enhanced Raman spectroscopy Drinking water monitoring Sensor Regulatory policy

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MCNP/NJOY Silicon Damage Function Synthesis and 1 MeV Equivalent Test Fluence Optimization

MCNP/NJOY Silicon Damage Function Synthesis and 1 MeV Equiva...

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2024 Transactions of the American Nuclear Society on Winter Conference and Expo, ANS 2024

作者： Johnson, Joseph Kimball, Taylor Sjoden, Glenn E. Nuclear Engineering Program Department of Civil and Environmental Engineering University of Utah United States

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Development of a hazard risk map for assessing pedestrian risk in urban flash floods:A case study in Cúcuta,Colombia

River

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River 2024年第1期3卷 8-23页

作者： Gerald Augusto Corzo Perez Diego Ivan Sanchez Tapiero Manuel Antonio Contreras Martínez Chris Zevenbergen IHE Delft Institute for Water Education Hydroinformatics and Digital InnovationDelftThe Netherlands Master in Environmental Engineering Civil Engineering ProgramFaculty of Engineering and ArchitectureUniversity of PamplonaPamplonaColombia Faculty of Civil Engineering and Geosciences Delft University of TechnologyDelftThe Netherlands

The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for *** trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians,which is often overlooked in traditional flood hazard *** study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods,with a specific focus on Cúcuta,*** methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological *** computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6,2020,using drones for digital elevation model data *** Cúcuta,where flood risk is high and drainage infrastructure is limited,the PCSWMM(Computer-based Urban Stormwater Management Model)was calibrated and validated to simulate extreme flood *** model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban *** return periods(5,10,50,100),with extreme rainfall of 3 h,were used to estimate the variability of the risk *** output of the model was analyzed,and an integrated and time-varying comparison of the results was *** show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme ***,from 5 to 100 years return period,the percentage of area at risk increased from 9.6%to 16.6%.The pedestrian sensitivity appears much higher than the increase in velocities or water depth *** study identified medium to high-risk locations,which are dynamic in *** can conclude dynamics are spatiotemporal,and the added information layer of pedestrians brings vulnerability information that is a

关键词： hazard and risk mapping pedestrian risk urban flood

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Implementation of Dama Mandala Application in Realizing Smart City Vision in Denpasar City 10

Implementation of Dama Mandala Application in Realizing Smar...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Wedagama, Dewa Ayu Trisna Adhiswari Indrashwara, Decky Cipta Agung Ananda Putra, I Gusti Universitas Pendidikan Nasional Environmental Engineering Study Program Denpasar Indonesia Universitas Pendidikan Nasional Civil Engineering Study Program Denpasar Indonesia

ISBN: (纸本)9798350363104

The incorporation of information and communication technology (ICT) to improve infrastructure and establish a efficient and responsive environment is a key element of Denpasar Citys ambition to evolve into a Smart City. This research delves into the utilization of E Government, via the Dama Mandala app underscoring its significance in this transition. Through surveys and interviews employing a methodology the study demonstrates that the Dama Mandala app has greatly enriched the lives of Denpasar Citys inhabitants by granting them access to government services and information online. These results imply that the app plays a role, in realizing Denpasar Citys aspiration of becoming a culture oriented Smart City advancing towards the vision of Denpasar Maju. © 2024 IEEE.

关键词： Smart city

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Understanding the interplay of sludge characteristics and dewatering in water treatment plants: a review

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environmental Technology Reviews 2024年第1期13卷 794-813页

作者： Paffrath, Silvia F. Carissimi, Elvis Schner, Jéssica C. N. Ferrari, Keila F. H. Etchepare, Ramiro G. Graduate Program in Water Resources and Environmental Engineering Federal University of Paraná Curitiba Brazil Department of Sanitary and Environmental Engineering Federal University of Santa Maria Santa Maria Brazil Graduate Program in Civil Engineering Federal University of Santa Maria Santa Maria Brazil Department of Hydraulics Sanitation Federal University of Paraná Curitiba Brazil

In the realm of water supply and the management of residues arising from water treatment plants (WTP), an essential challenge lies in understanding and characterising sludge, and assessing whether these characteristics impact sludge dewatering, an essential step to reduce the management costs of these wastes. This review focuses specifically on the characterisation of sludge properties relevant to dewatering performance, addressing an underexplored aspect within recent literature on water treatment plant sludge (WTS). Unlike prior studies, which predominantly focus on sludge reuse after dewatering, this review systematically examines the direct correlations and interdependencies between sludge characteristics and dewatering outcomes, addressing gaps in literature. A bibliometric analysis of global WTS research revealed an annual average of fewer than 14 publications since 1957. Keywords related to sludge characteristics are notably scarce in connection with ‘dewatering’, highlighting a specific gap that this review aims to address. This review categorises sludge characteristics into ‘micro-properties’ and ‘macro-properties’, providing detailed descriptions of each. The latter primarily influence dewatering and are generally unmeasured in WTPs due to the inherent difficulties in their determination. However, it’s possible to derive macro-properties from micro-properties, establishing their relationship and subsequently selecting an appropriate dewatering technique. Main findings underscore direct proportionality between: viscosity (micro) and capillary suction time–CST (macro), solid concentration–SC (micro) and CST;and inverse proportionality between: SC and specific resistance to filtration–SRF (macro). These findings contribute a refined understanding of parameters affecting sludge dewatering processes, supporting further research focused on enhancing efficiency in WTS management. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Dewatering

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

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Frontiers of Structural and civil engineering 2022年第9期16卷 1153-1169页

作者： Van Quan TRAN Van Loi GIAP Dinh Phien VU Riya Catherine GEORGE Lanh Si HO Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement ***,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement *** 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 *** 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 *** 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 *** 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 hybrid machine learning model to estimate self-compacting concrete compressive strength

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Frontiers of Structural and civil engineering 2022年第8期16卷 990-1002页

作者： Hai-Bang LY Thuy-Anh NGUYEN Binh Thai PHAM May Huu NGUYEN Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

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 *** 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 *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

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Effects of climate change in winter ice cover and ice thickness in flooding: a case study of Grand River, Ohio, USA

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ISH Journal of Hydraulic engineering 2022年第S1期28卷 503-517页

作者： Lamichhane, Niraj Sharma, Suresh Subedi, Abhijit Sharma Civil and Environmental Engineering Program Youngstown State University YoungstownOH United States

The major objective of this research is to assess the effects of winter ice cover and simulate the flooding within bridge vicinity of the Grand River, Ohio, the USA for both in the historical period (1959–2014) and future period (2015–2098). The LiDAR data along with field-verified survey data were utilized for the calibration and validation of HEC-RAS. The stage increments up to 6.75 ft were detected at the upstream section of the Bridge due to historical ice jams. Moreover, the increment in the inundation area varied from 24% to 52% for various percentile winter flows. The percentage increase in the inundation area was highest for the 25-percentile flow than the higher percentile flow when the same thickness of ice cover was used in the simulation. The estimated ice thickness using Stefan’s equation for various emission scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) for three climate models were found to be decreasing in the future period. As per the standard practice in the United States, authors have used U.S. Customary Unit System (fps unit system) in this paper. © 2020 Indian Society for Hydraulics.

关键词： Climate models

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