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Biodegradability of bioplastics in different aquatic environments:A systematic review

Journal of environmental Sciences 2024年第8期142卷 169-181页

作者： Maria Cristina Lavagnolo Valentina Poli Anna Maria Zampini Valentina Grossule Department of civil Environmental and Architectural Engineering-Laboratory of Environmental EngineeringUniversity of PadovaLungargine Rovetta 8Padova 35100Italy

Bioplastics were first introduced as environmentally friendly materials,with properties similar to those of conventional plastics.A bioplastic is defined as biodegradable if it can be decomposed into carbon dioxide under aerobic degradation,or methane and CO_(2)under anaerobic conditions,inorganic compounds,and new cellular biomass,by the action of naturally occurring microorganisms.This definition however does not provide any information on the environmental conditions,timescale and extent at which decomposition processes should occur.With regard to the aquatic environment,recognized standards have been established to assess the ability of plastics to undergo biodegradation;however,these standards fail to provide clear targets to be met to allow labelling of a bioplastic as biodegradable.Moreover,these standards grant the user an extensive leeway in the choice of process parameters.For these reasons,the comparison of results deriving from different studies is challenging.The authors analysed and discussed the degree of biodegradability of a series of biodegradable bioplastics in aquatic environments(both fresh and salt water)using the results obtained in the laboratory and from on-site testing in the context of different research studies.Biochemical Oxygen Demand(BOD),CO_(2)evolution,surface erosion and weight loss were the main parameters used by researchers to describe the percentage of biodegradation.The results showed a large variability both in weight loss and BOD,even when evaluating the same type of bioplastics.This confirms the need for a reference range of values to be established with regard to parameters applied in defining the biodegradability of bioplastics.

关键词： Biodegradation Bioplastic Plastic Natural environment Plastic waste

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Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

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Journal of Traffic and Transportation engineering(English Edition) 2024年第3期11卷 381-405页

作者： Jian Liu Fangyu Liu Linbing Wang Department of civil and environmental engineering Virginia Polytechnic Institute and State University School of environmental CivilAgricultural and Mechanical EngineeringUniversity of Georgia Illinois Center for Transportation Department of Civil and Environmental EngineeringUniversity of Illinois Urbana-Champaign

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning （ML） and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity（Gmb） and air void （VV）) and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization （MOGWO）algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO2emission.The results indicated that least squares support vector regression （LSSVR） and e Xtreme gradient boosting （XGBoost） achieved the highest prediction accuracies （correlation coefficient:0.92 for VV and 0.96 for Gmb）.The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO2emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

关键词： Asphalt mix design Machine learning MOGWO CO 2 emission Volumetric properties

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Investigation of material composition,design,and performance of open-graded asphalt mixtures for semi-flexible pavement:A comprehensive experimental study

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Journal of Traffic and Transportation engineering(English Edition) 2024年第1期11卷 92-116页

作者： Mohammad Taghipoor Abolfazl Hassani Mohammad M.Karimi Department of civil and environmental engineering Tarbiat Modares UniversityTehranIran

The primary goal of this study is the design and construction of semi-flexible pavement(SFP)mixture in accordance with the engineering and mechanical criteria.This study involves the use of a range of gradation curves,air void contents,cellulose and synthesized fibers,and neat and modified asphalt binders to prepare the open-graded asphalt(OGA)mixtures.To analyze the characteristics of these mixtures,a variety of test,namely binder drainage,semi-circular bending(SCB),Cantabro,wheel tracking,indirect tensile strength(ITS),and permeability tests were conducted.Additionally,to analyze the prepared grouting material,flexural strength,compressive strength,and fluidity tests were conducted.In the final stage,SFP was compared to HMA in terms of engineering characteristics and performance.According to the results,SFP was more resistant to skid,rutting,fire,and moisture damage,while HMA had a better performance in fracture tests,including SCB test.According to the results of the mechanical performance tests conducted on OGA mixtures,the highest and lowest values for air void content to achieve the highest mechanical performance level were 30%-35%and 25%,respectively.Also,based on the laboratory results,it was determined that the required void ratio for constructing OGA mixtures was 24%-26%based on the bitumen type and fibers amount in the mixture.Finally,SFP mixture can be regarded as a viable alternative to common pavements thanks to its high resistance to rutting and moisture damage,long freezing-thawing fatigue life,and adequate fire and skid resistance.

关键词： Semi-flexible pavement mixture Open-graded asphalt mixture engineering properties Cantabro test Hot mix asphalt

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Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

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Earthquake engineering and engineering Vibration 2024年第2期23卷 345-358页

作者： Zan Zhou Thomas Man-Hoi Lok Wan-Huan Zhou Department of civil and environmental engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

关键词： surface wave inversion analysis shear-wave velocity profile deep neural network genetic algorithm

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Amorphous electrocatalysts for urea oxidation reaction

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Progress in Natural Science:Materials International 2024年第2期34卷 362-375页

作者： Fenghui Guo Dongle Cheng Qian Chen Hao Liu Zhiliang Wu Ning Han Bing-Jie Ni Zhijie Chen School of Materials Science and Technology China University of Geosciences Shenzhen Academy of Metrology and Quality Inspection Centre for Technology in Water and Wastewater School of Civil and Environmental Engineering University of Technology Sydney College of Safety and environmental engineering Shandong University of Science and Technology Institute of Yellow River Delta Earth Surface Processes and Ecological Integrity Shandong University of Science and Technology discipline of Chemical engineering Western Australian School of Mines: Minerals Energy and Chemical Engineering Curtin University Department of Materials engineering School of civil and environmental engineering The University of New South Wales

Electrochemical urea oxidation reaction(UOR) is a promising alternative to oxygen evolution reaction(OER) for realizing energy-saving hydrogen production. Developing efficient electrocatalysts for UOR becomes a central challenge. Recently, amorphous materials have been extensively used as UOR catalysts because of their numerous defective sites and flexible electronic properties. In this review, recent advancements in the development of amorphous UOR electrocatalysts are analyzed. The UOR mechanism is discussed, and the design of amorphous catalysts is then analyzed. The main catalyst design strategies are illustrated, including nanostructure control,heteroatom doping, composition regulation, and heterostructure construction. Also, electrocatalysts' structureperformance correlation is interpreted. Perspectives in this field are proposed for guiding future studies on the development of high-performance amorphous catalysts towards energy sustainability.

关键词： Catalyst design Component regulation Heteroatom doping Heterostructures Hydrogen energy

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Surrogate modeling for unsaturated infiltration via the physics and equality-constrained artificial neural networks

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Journal of Rock Mechanics and Geotechnical engineering 2024年第6期16卷 2282-2295页

作者： Peng Lan Jingjing Su Sheng Zhang School of civil engineering Central South UniversityChangsha410075China discipline of civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringThe University of NewcastleCallaghanNSW2308Australia Qinghai University Qinghai810016China

Machine learning(ML)provides a new surrogate method for investigating groundwater flow dynamics in unsaturated soils.Traditional pure data-driven methods(e.***.deep neural network,DNN)can provide rapid predictions,but they do require sufficient on-site data for accurate training,and lack interpretability to the physical processes within the data.In this paper,we provide a physics and equalityconstrained artificial neural network(PECANN),to derive unsaturated infiltration solutions with a small amount of initial and boundary data.PECANN takes the physics-informed neural network(PINN)as a foundation,encodes the unsaturated infiltration physical laws(i.e.Richards equation,RE)into the loss function,and uses the augmented Lagrangian method to constrain the learning process of the solutions of RE by adding stronger penalty for the initial and boundary conditions.Four unsaturated infiltration cases are designed to test the training performance of PECANN,i.***.one-dimensional(1D)steady-state unsaturated infiltration,1D transient-state infiltration,two-dimensional(2D)transient-state infiltration,and 1D coupled unsaturated infiltration and deformation.The predicted results of PECANN are compared with the finite difference solutions or analytical solutions.The results indicate that PECANN can accurately capture the variations of pressure head during the unsaturated infiltration,and present higher precision and robustness than DNN and PINN.It is also revealed that PECANN can achieve the same accuracy as the finite difference method with fewer initial and boundary training data.Additionally,we investigate the effect of the hyperparameters of PECANN on solving RE problem.PECANN provides an effective tool for simulating unsaturated infiltration.

关键词： Richards equation(RE) Unsaturated infiltration Data-driven solutions Numerical modeling Machine learning(ML)

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Removal of atmospheric methane by soil ecosystems and its controlling variables from microbial to global scales

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Pedosphere 2024年第1期34卷 15-18页

作者： Hojeong KANG Jaehyun LEE School of civil and environmental engineering Yonsei UniversitySeoul 03722Korea

Methane (CH_(4)), a potent greenhouse gas, plays a pivotal role in the dynamics of climate change. While CH_(4) emissions have been widely investigated,biological removal of CH_(4) by upland soils has been less explored. Understanding the mechanisms and factors affecting CH_(4) oxidation in soils is of paramount importance for devising successful mitigation strategies. This perspective paper discusses different types of aerobic methanotrophs and their activities under varying environmental conditions, highlighting the significant contribution of soil ecosystems to global CH_(4) sinks. We emphasize the need for in-depth research on variables controlling CH_(4) sinks on different spatiotemporal scales and the exploration of previously unidentified CH_(4) sinks, such as deserts and areas of glacier retreat.

关键词： soil ecosystems controlling

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A reinforcement learning approach for thermostat setpoint preference learning

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Building Simulation 2024年第1期17卷 131-146页

作者： Hussein Elehwany Mohamed Ouf Burak Gunay Nunzio Cotrufo Jean-Simon Venne Department of environmental engineering Carleton UniversityOttawaCanada Departmentof Building Civiland Environmental EngineeringConcordia UniversityMontrealCanada BrainBox Al Inc. MontrealCanada

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

关键词： reinforcement learning preference learning occupant-centric controls smart thermostats off-policy learning

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MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators

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Journal of Management Analytics 2024年第1期11卷 45-61页

作者： Jun Ye Shigui Du Rui Yong School of civil and environmental engineering Ningbo UniversityNingboPeople’s Republic of China

Motivated based on the trigonometric t-norm and t-conorm,the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making(MCDM)technique in an inconsistent and indeterminate circumstance.To realize the aims,this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs,which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions,and presents the SvNN trigonometric weighted average and geometric operators and their properties.Next,an MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs.In the end,an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique.

关键词： Single-valued neutrosophic number trigonometric t-norm and t-conorm trigonometric weighted aggregation operator decision making

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Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

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Journal of Rock Mechanics and Geotechnical engineering 2024年第1期16卷 167-182页

作者： Amit Jaiswal A.K.Verma T.N.Singh Department of civil and environmental engineering Indian Institute of Technology PatnaPatna801106India

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

关键词： Rock mass classification Kinematic analysis Slope stability Himalayan road Static and dynamic conditions

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