Reliability-based life-cycle cost seismic design optimization of coastal bridge piers with nonuniform corrosion using different materials

Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonuniform corrosion using different materials.In this study,a reliability-based design optimization(RBDO)procedure is improved for the design of coastal bridge piers using six groups of commonly used materials,i.e.,normal performance concrete(NPC)with black steel(BS)rebar,high strength steel(HSS)rebar,epoxy coated(EC)rebar,and stainless steel(SS)rebar(named NPC-BS,NPC-HSS,NPC-EC,and NPC-SS,respectively),NPC with BS with silane soakage on the pier surface(named NPC-Silane),and high-performance concrete(HPC)with BS rebar(named HPC-BS).First,the RBDO procedure is improved for the design optimization of coastal bridge piers,and a bridge is selected to illustrate the procedure.Then,reliability analysis of the pier designed with each group of materials is carried out to obtain the time-dependent reliability in terms of the ultimate and serviceability performances.Next,the repair time of the pier is predicted based on the time-dependent reliability indices.Finally,the time-dependent LCCs for the pier are obtained for the selection of the optimal design.

Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots

Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial applications.Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development,providing several alternatives beyond the famous ChatGPT.However,training,fine-tuning,and updating such intelligent chatbots consume substantial amounts of electricity,resulting in significant carbon emissions.The research and development of all intelligent LLMs and software,hardware manufacturing(e.g.,graphics processing units and supercomputers),related data/operations management,and material recycling supporting chatbot services are associated with carbon emissions to varying extents.Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact.In this work,we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots.Based on a life-cycle and interaction analysis of these phases,we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints.While anticipating the enormous potential of this advanced technology and its products,we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.

Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys

In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector module was presented.A high-precision forward prediction model based on deep neural networks was developed to implement these two parts.Of utmost importance,domain knowledge-guided inverse design networks(DKIDNs)and regular inverse design networks(RIDNs)were also developed.The forward prediction model possesses a coefficient of determination(R^(2))of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set.Furthermore,the DKIDNs model exhibits superior performance compared to the RIDNs model.It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.

Design Strategies Applied to Classroom's Daylight Design Optimization of Classrooms Design

This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virtual models of classrooms were evaluated using the Radiance software.We used a methodology that allowed us to determine the luminous conditions under different types of skies,seasons of the year and times of the day.The evaluation of the typologies was performed based on three defined criteria,in order to achieve the stated design objectives.We defined the optimal solutions for each orientation and,finally,we stated design recommendations for daylit classrooms to ensure the visual comfort of the students.These recommendations link all that found in the initial analysis with that found in the optimization stage.

Design strategies and insights of flexible infrared optoelectronic sensors

Infrared optoelectronic sensing is the core of many critical applications such as night vision,health and medication,military,space exploration,etc.Further including mechanical flexibility as a new dimension enables novel features of adaptability and conformability,promising for developing next-generation optoelectronic sensory applications toward reduced size,weight,price,power consumption,and enhanced performance(SWaP^(3)).However,in this emerging research frontier,challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated systems.Therefore,we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors,including the fundamentals of infrared photodetectors,selection of materials and device architectures,fabrication techniques and design strategies,and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image sensing.Finally,this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials,covering challenges in materials development and device micro-/nanofabrication.Benchmarks for scaling these techniques across fabrication,performance,and integration are presented,alongside perspectives on potential applications in medication and health,biomimetic vision,and neuromorphic sensory systems,etc.

Seismic isolation design and resilience improvement of railway station considering the influence of near-fault pulse-like ground motions

To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The seismic resilience of the no-isolation railway stations(NIRS)and the isolation railway stations(IRS)were compared to provide a numerical result of the improvement in resilience.The results show that in the station isolation design,the station's functional requirements and structural characteristics should be considered and the appropriate placement of isolation bearings is under the waiting room.Under the action of a rare earthquake,the repair cost,repair time,rate of harm and death of the IRS were decreased by 8.04 million,18.30 days,6.93×10^(-3)and 1.21×10^(-3),respectively,when compared to the NIRS.The IRS received a seismic resilience grade of three-stars and the NIRS only one-star,indicating that rational isolation design improves the seismic resilience of stations.Thus,for the design of stations close to earthquake faults,it is suggested to utilize appropriate isolation techniques to improve their seismic resilience.

基于Altium Designer软件的电子控制技术中电路设计探究

在正常的电路设计方案中,需要软硬件的互相搭配使用,普通的电路运用收稿方式或简单的办公辅助软件来完成绘制,但在面对复杂情况下的电路,专业的EDA辅助设计软件作用可以显而易见,以该辅助软件设计如SCH(电路原理图)与PCB(印制电路板)文件方面的工作。电子控制技术中电路随着时代的进步而不断创新,新型器件的不断迭代更新,其中的电路设计也变得复杂且繁琐,传统的电路设计已无法胜任最新电路的完善和使用。此时运用AD16(Altium Designer)这款辅助设计软件进行电路设计就可以事半功倍,AD都是复杂电路设计中的高效工具。

Construction of project quality health monitoring system based on life-cycle theory

In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.

水田动力底盘逆向建模与质心验证——基于Geomagic Design X

针对目前农业机械仿真和优化模型精度不高的问题,提出了一种构建精确模型的逆向建模方法。应用光学扫描仪采集点云数据,基于Geomagic Design X处理点云数据并重构零部件模型,在SolidWorks中完成水田动力底盘的装配。以VP6D-CQ型水田动力底盘为例,将测得的实际质心位置与所建的模型质心位置进行比较,并对提出的逆向建模方法进行了试验验证以及误差分析。结果表明:总质量、x坐标、y坐标、z坐标的误差值分别为4.55%、3.62%、2.23%、4.81%,误差值均在5%内。此方法可构建准确的三维模型,为后续仿真优化数据精确性奠定了基础,与传统的研发相比较,可缩短农业机械研发周期、降低设计成本。

App Designer辅助教学方法探索与实践——以软件无线电课程为例

软件无线电作为通信学科的必修课程,具有承上启下的重要作用。因课程原理抽象、理论公式复杂,学员在学习过程存在理解掌握难、实践应用难等问题。通过对当前软件无线电教学过程中存在的不足与困难进行分析,提出利用MATLAB App Designer辅助课程教学,从教学准备、课堂互动、自主实验、反馈提升四个方面开展课程教学改革。实践表明,该方法对于提升学员的工程思维、系统思维具有显著效果,同时可以增强学员理论联系实际的能力。

Characterization and evaluation of brittleness of deep bedded sandstone from the perspective of the whole life-cycle evolution process

The quantitative determination and evaluation of rock brittleness are crucial for the estimation of excavation efficiency and the improvement of hydraulic fracturing efficiency.Therefore,a“three-stage”triaxial loading and unloading stress path is designed and proposed.Subsequently,six brittleness indices are selected.In addition,the evolution characteristics of the six brittleness indices selected are characterized based on the bedding effect and the effect of confining pressure.Then,the entropy weight method(EWM)is introduced to assign weight to the six brittleness indices,and the comprehensive brittleness index Bcis defined and evaluated.Next,the new brittleness classification standard is determined,and the brittleness differences between the two stress paths are quantified.Finally,compared with the previous evaluation methods,the rationality of the proposed comprehensive brittleness index Bcis also verified.These results indicate that the proposed brittleness index Bccan reflect the brittle characteristics of deep bedded sandstone from the perspective of the whole life-cycle evolution process.Accordingly,the method proposed seems to offer reliable evaluations of the brittleness of deep bedded sandstone in deep engineering practices,although further validation is necessary.

Life-cycle failure probability analysis of deteriorated RC bridges under multiple hazards of earthquakes and strong winds

Engineering structures may be exposed to one or more extreme hazards during their life-cycles.Current structural design specifications usually treat multiple hazards separately in designing structures and there is a limited probabilistic basis on extreme load combinations.Additionally,the performance of engineering structures will be deteriorated by the aggressive environments during their service periods,such as chloride attack,concrete carbonation,and wind-induced fatigue.This study presents a probabilistic methodology to assess the time-dependent failure probability of RC bridges with chloride-induced corrosion under the multiple hazards of earthquakes and strong winds.The loss of cross-section area of reinforcements and the reduction in strength of reinforcing steel and concrete cover induced by the chloride attack are considered.Moreover,the Poisson model is employed to obtain the occurrence probabilities of the individual and concurrent earthquake and strong wind events.The convolution integral is used to determine the joint probability distribution of combined load effects under simultaneous earthquakes and strong winds.Numerical results indicate that the structural failure probability under multiple hazards increases significantly during the bridge′s life-cycle due to the chloride corrosion effect.The contribution of each hazard event on the total structural failure probability varies with time.Thus,neglecting the combined influences of multiple hazards and chloride-induced corrosion may bring erroneous predictions in failure probability estimates of RC bridges.

Life-cycle Assessment of Carbon Dioxide Capture for Enhanced Oil Recovery

The development and deployment of Carbon dioxide Capture and Storage （CCS） technology is a cornerstone of the Norwegian government＇s climate strategy. A number of projects are currently evaluated/planned along the Norwegian West Coast, one at Tjeldbergodden. COe from this project will be utilized in part for enhanced oil recovery in the Halten oil field, in the Norwegian Sea. We study a potential design of such a system. A combined cycle power plant with a gross power output of 832 MW is combined with CO2 capture plant based on a post-combustion capture using amines as a solvent. The captured CO2 is used for enhanced oil recovery （EOR）. We employ a hybrid life-cycle assessment （LCA） method to assess the environmental impacts of the system. The study focuses on the modifications and operations of the platform during EOR. We allocate the impacts connected to the capture of CO2 to electricity production, and the impacts connected to the transport and storage of CO2 to the oil produced. Our study shows a substantial reduction of the greenhouse gas emissions from power production by 80% to 75 g·（kW·h）^-1. It also indicates a reduction of the emissions associated with oil production per unit oil produced, mostly due to the increased oil production. Reductions are especially significant if the additional power demand due to EOR leads to power supply from the land.

A Blockchain-Based Life-Cycle Environmental Management Framework for Hospitals in the COVID-19 Context

During the coronavirus disease 2019 (COVID-19) emergency, many hospitals were built or renovated around the world to meet the challenges posed by the rising number of infected cases. Environmental management in the hospital life cycle is vital in preventing nosocomial infection and includes many infection control procedures. In certain urgent situations, a hospital must be completed quickly, and work process approval and supervision must therefore be accelerated. Thus, many works cannot be checked in detail. This results in a lack of work liability control and increases the difficulty of ensuring the fulfillment of key infection prevention measures. This study investigates how blockchain technology can transform the work quality inspection workflow to assist in nosocomial infection control under a fast delivery requirement. A blockchain-based life-cycle environmental management framework is proposed to track the fulfillment of crucial infection control measures in the design, construction, and operation stages of hospitals. The proposed framework allows for work quality checking after the work is completed, when some work cannot be checked on time. Illustrative use cases are selected to demonstrate the capabilities of the developed solution. This study provides new insights into applying blockchain technology to address the challenge of environmental management brought by rapid delivery requirements.

Alloy design for laser powder bed fusion additive manufacturing:a critical review

Metal additive manufacturing(AM)has been extensively studied in recent decades.Despite the significant progress achieved in manufacturing complex shapes and structures,challenges such as severe cracking when using existing alloys for laser powder bed fusion(L-PBF)AM have persisted.These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes,overlooking the fast cooling rates,steep temperature gradients and multiple thermal cycles of L-PBF.To address this,there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies.This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF.It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys.The review begins by discussing the features of the L-PBF processes,focusing on rapid solidification and intrinsic heat treatment.Next,the printability of the four main existing alloys(Fe-,Ni-,Al-and Ti-based alloys)is critically assessed,with a comparison of their conventional weldability.It was found that the weldability criteria are not always applicable in estimating printability.Furthermore,the review presents recent advances in alloy development and associated strategies,categorizing them into crack mitigation-oriented,microstructure manipulation-oriented and machine learning-assisted approaches.Lastly,an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

Life-cycle CO_2 Emissions and Their Driving Factors in Construction Sector in China

As the construction sector is a major energy consumer and thus a significant contributor of CO_2 emissions in China,it is important to consider carbon reduction in this industry.This study analyzed six life-cycle stages and calculated the life-cycle CO_2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them.Results show that building materials production was the key stage for carbon reduction in the construction sector,followed by the building operation stage.External variables,e.g.,economic growth,industrial structure,urbanization,price fluctuation,and marketization,were significantly correlated with the emission intensity of the construction sector.Specifically,economic growth exhibited an inverted U-shaped relation with CO_2 emissions per capita and per area during the period examined.Secondary industry and land urbanization were negatively correlated with CO_2 emission intensity indicators from the construction sector,whereas tertiary industry and urbanization were positively correlated.Price indices and marketization had negative effects on CO_2 emission intensity.The policy implications of our findings are that cleaner technologies should be encouraged for cement providers,and green purchasing rules for the construction sector should also be established.Pricing tools(e.g.,resource taxes)could help to adjust the demand for raw materials and energy.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Comparative Life-cycle Assessment of Sheet Molding Compound Reinforced by Natural Fiber vs. Glass Fiber

We present comparative life-cycle assessments of three fiber-reinforced sheet molding compounds （SMCs） using kenaf fiber, glass fiber and soy protein resin. Sheet molding compounds for automotive applications are typically made of unsaturated polyester and glass fibers. Replacing these with kenaf fiber or soy protein offers potential environmental benefits. A soy-based resin, maleated acrylated epoxidized soy oil （MAESO）, was synthesized from refined soybean oil. Kenaf fiber and polyester resins were used to make SMC 1 composites, while SMC2 composites were made from kenaf fiber and a resin blend of 20% MASEO and 80% unsaturated polyester. Both exhibited good physical and mechanical properties, though neither was as strong as glass fiber reinforced polyester SMC. The functional unit was defined as mass to achieve equal stiffness and stability for the manufacture of interior parts for automobiles. The life-cycle assessments were done on SMCI, SMC2 and glass fiber reinforced SMC. The material and energy balances from producing one functional unit of three composites were collected from lab experiments and the literature. Key environmental measures were computed using SimaPro software. Kenaf fiber-reinforced SMC composites （SMC1 and SMC2） performed better than glass fiber-reinforced SMC in every environmental category. The global warming potentials of kenaf fiber-reinforced SMC （SMCI） and kenaf soy resin-based SMC （SMC2） were 45% and 58%, respectively, of glass fiber-reinforced SMC. Thus, we have demonstrated significant ecological benefit from replacing glass fiber reinforced SMC with soy-based resin and natural fiber.