As the level of construction informatization continues to rise, BIM technology is bringing new directions and opportunities to cost management in engineering projects. This article focuses on the full-process manageme...As the level of construction informatization continues to rise, BIM technology is bringing new directions and opportunities to cost management in engineering projects. This article focuses on the full-process management approach for project cost control from initiation to completion, thoroughly examining the specific applications of BIM technology at each stage and its practical value. It first outlines that full-process management encompasses multiple phases, including investment estimation at project initiation, preliminary design budgeting, detailed design budgeting, cost control during bidding and procurement, and final settlement after project acceptance. The article then elaborates on the core characteristics of BIM technology—information integration, visualization, collaboration, and simulation. Next, it critically analyzes the evident shortcomings of traditional management methods, such as fragmented information, redundant data entry, slow adjustment processes, and low team coordination efficiency. A key section details how BIM technology enables rapid acquisition of complete quantity data required for investment estimation and preliminary design budgeting, significantly improving accuracy by leveraging existing price databases. In the stages of construction drawing budgeting and bidding procurement, precise 3D modeling generates detailed bill of quantities, serving as a crucial reference for setting bid control prices and calculating tender quotations. During actual construction, integrating model data with cost management systems allows real-time monitoring and adjustment of progress payments, analysis of cost changes due to contract modifications. Finally, the article summarizes ongoing challenges in the widespread adoption of BIM technology, including inconsistent technical standards, insufficient funding for hardware, inadequate expertise among professionals. It also forecasts that future BIM technology will closely integrate with emerging technologies展开更多
The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machin...The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machine learning(ML),has rapidly become a key tool for the design and optimization of PSCs.However,current ML models often oversimplify the design of PSCs at the device level,making it difficult to capture the complexity of their multi-scale features.Moreover,they are constrained by relatively small and specialized datasets,which limits their generalizability across diverse device architectures and fabrication methods.In this work,we developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features.This framework offers significant advantages in both sample diversity and feature richness.It combines material selection,fabrication processes,and environmental factors to provide a more accurate and comprehensive optimization solution for PSCs.We addressed challenges from data diversity and heterogeneity through feature engineering and model training,which results in a highly generalizable PSC performance prediction model with comparable prediction error to small-scale models.The framework enables precise optimization of specific features for any PSCs,and provides valuable insights for designing high-performance photovoltaic devices.展开更多
Pressure-preserved coring is a highly effective technique for preserving the structural integrity and fluid composition of reservoir cores.However,the lack of specialized testing methodologies hinders the precise eval...Pressure-preserved coring is a highly effective technique for preserving the structural integrity and fluid composition of reservoir cores.However,the lack of specialized testing methodologies hinders the precise evaluation of pressure-preserved core fidelity parameters.To address this challenge,a comprehensive study was conducted on the entire process of pressure-preserved transfer,cutting,and testing,culminating in the development of a pressure-preserved computed tomography(CT)scanning device.The critical technical challenges encountered during the development process were systematically analyzed through mechanical testing,CT scanning,and numerical simulations.Special emphasis was placed on material influencesduring testing,mechanical assembly interactions,and the accuracy of key parameter measurements in oil and gas exploration.Through comparative analysis and multitiered validation methods,polyether ether ketone(PEEK)material was ultimately selected as the key component of the coring device.The simulation and experimental results demonstrated that PEEK,with a maximum tensile strength of 104 MPa,sufficiently meets most core breakage thresholds of 93.942 MPa.Furthermore,CT scanning revealed a porosity measurement error of only 0.111%,confirmingthe reliability of the pressure-preserved CT test equipment.These findingsoffer valuable guidance for improving the precision of pressure-preserved core testing in deep oil and gas reservoirs.展开更多
摘要As the level of construction informatization continues to rise, BIM technology is bringing new directions and opportunities to cost management in engineering projects. This article focuses on the full-process management approach for project cost control from initiation to completion, thoroughly examining the specific applications of BIM technology at each stage and its practical value. It first outlines that full-process management encompasses multiple phases, including investment estimation at project initiation, preliminary design budgeting, detailed design budgeting, cost control during bidding and procurement, and final settlement after project acceptance. The article then elaborates on the core characteristics of BIM technology—information integration, visualization, collaboration, and simulation. Next, it critically analyzes the evident shortcomings of traditional management methods, such as fragmented information, redundant data entry, slow adjustment processes, and low team coordination efficiency. A key section details how BIM technology enables rapid acquisition of complete quantity data required for investment estimation and preliminary design budgeting, significantly improving accuracy by leveraging existing price databases. In the stages of construction drawing budgeting and bidding procurement, precise 3D modeling generates detailed bill of quantities, serving as a crucial reference for setting bid control prices and calculating tender quotations. During actual construction, integrating model data with cost management systems allows real-time monitoring and adjustment of progress payments, analysis of cost changes due to contract modifications. Finally, the article summarizes ongoing challenges in the widespread adoption of BIM technology, including inconsistent technical standards, insufficient funding for hardware, inadequate expertise among professionals. It also forecasts that future BIM technology will closely integrate with emerging technologies
基金supported by the National Natural Science Foundation of China(52302333 to Bai Y,52373233 to Sun Y)the SIAT International Joint Lab Project(E3G113 to Sun Y)+1 种基金the Shenzhen Science and Technology Program(KQTD20221101093647058 to Bai Y and Sun Y,Shenzhen KJZD20231025152759001 to Bai Y)the Guangdong Basic and Applied Basic Research Foundation(2023A1515012788 to Bai Y,2024A1515010679 to Sun Y).
摘要The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machine learning(ML),has rapidly become a key tool for the design and optimization of PSCs.However,current ML models often oversimplify the design of PSCs at the device level,making it difficult to capture the complexity of their multi-scale features.Moreover,they are constrained by relatively small and specialized datasets,which limits their generalizability across diverse device architectures and fabrication methods.In this work,we developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features.This framework offers significant advantages in both sample diversity and feature richness.It combines material selection,fabrication processes,and environmental factors to provide a more accurate and comprehensive optimization solution for PSCs.We addressed challenges from data diversity and heterogeneity through feature engineering and model training,which results in a highly generalizable PSC performance prediction model with comparable prediction error to small-scale models.The framework enables precise optimization of specific features for any PSCs,and provides valuable insights for designing high-performance photovoltaic devices.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1003901)the National Natural Science Foundation of China(Grant Nos.52304146 and 52104142).
摘要Pressure-preserved coring is a highly effective technique for preserving the structural integrity and fluid composition of reservoir cores.However,the lack of specialized testing methodologies hinders the precise evaluation of pressure-preserved core fidelity parameters.To address this challenge,a comprehensive study was conducted on the entire process of pressure-preserved transfer,cutting,and testing,culminating in the development of a pressure-preserved computed tomography(CT)scanning device.The critical technical challenges encountered during the development process were systematically analyzed through mechanical testing,CT scanning,and numerical simulations.Special emphasis was placed on material influencesduring testing,mechanical assembly interactions,and the accuracy of key parameter measurements in oil and gas exploration.Through comparative analysis and multitiered validation methods,polyether ether ketone(PEEK)material was ultimately selected as the key component of the coring device.The simulation and experimental results demonstrated that PEEK,with a maximum tensile strength of 104 MPa,sufficiently meets most core breakage thresholds of 93.942 MPa.Furthermore,CT scanning revealed a porosity measurement error of only 0.111%,confirmingthe reliability of the pressure-preserved CT test equipment.These findingsoffer valuable guidance for improving the precision of pressure-preserved core testing in deep oil and gas reservoirs.