A BIM-based value for money assessment in public-private partnership: an overall review

Public-private partnerships (PPPs) have proliferated and adapted to public development in recent decades; within it, the value for money (VfM) assessment defines the feasibility of the project procurement model as one of the essential components of PPP. However, evaluating the VfM in PPPs remains problematic. Given concerns about PPP profitability, a more integrated VfM evaluation is urgently needed to manage multiple indicators along the project lifecycle. Building information management (BIM), popular in architecture, engineering, and construction, provides resources that could support the VfM to a great extent. This paper uses a review approach to identify the current issues that are affecting VfM assessments and suggests that BIM, functioning throughout the PPP lifecycle, could support decision-making in VfM processes in order to satisfy service targets

Experimental investigation and prediction of compressive strength of ultra-high performance concrete containing supplementary cementitious materials

Ultra-high performance concrete (UHPC) has superior mechanical properties and durability to normal strength concrete. However, the high amount of cement, high environmental impact, and initial cost are regarded as disadvantages, restricting its wider application. Incorporation of supplementary cementitious materials (SCMs) in UHPC is an effective way to reduce the amount of cement needed while contributing to the sustainability and cost. This paper investigates the mechanical properties and microstructure of UHPC containing fly ash (FA) and silica fume (SF) with the aim of contributing to this issue. The results indicate that, on the basis of 30% FA replacement, the incorporation of 10% and 20% SF showed equivalent or higher mechanical properties compared to the reference samples. The microstructure and pore volume of the UHPCs were also examined. Furthermore, to minimise the experimental workload of future studies, a prediction model is developed to predict the compressive strength of the UHPC using artificial neural networks (ANNs). The results indicate that the developed ANN model has high accuracy and can be used for the prediction of the compressive strength of UHPC with these SCMs

An ontology-based approach supporting holistic structural design with the consideration of safety, environmental impact and cost

Early stage decision-making for structural design critically influences the overall cost and environmental performance of buildings and infrastructure. However, the current approach often fails to consider the multi-perspectives of structural design, such as safety, environmental issues and cost in a comprehensive way. This paper presents a holistic approach based on knowledge processing (ontology) to facilitate a smarter decision-making process for early design stage by informing designers of the environmental impact and cost along with safety considerations. The approach can give a reasoning based quantitative understanding of how the design alternatives using different concrete materials can affect the ultimate overall performance. Embodied CO2 and cost are both considered along with safety criteria as indicative multi-perspectives to demonstrate the novelty of the approach. A case study of a concrete structural frame is used to explain how the proposed method can be used by structural designers when taking multi performance criteria into account. The major contribution of the paper lies on the creation of a holistic knowledge base which links through different knowledge across sectors to enable the structural engineer to come up with much more comprehensive decisions instead of individual single objective targeted delivery

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning

BackgroundTo explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).MethodsCuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data.ResultsPatients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples.ConclusionOur study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection

Developing a framework leveraging building information modelling to validate fire emergency evacuation

In fire emergency management, a delayed execution will cause a significant number of casualties. Conventional fire drills typically only identify a certain percentage of evacuation bottlenecks after the building has been constructed, which is hard to improve. This paper proposes an innovative framework to validate fire emergency evacuation at the early design stage. According to the experience and knowledge of fire emergency evacuation design, the proposed framework also introduces a seamless two-way information channel to embed fire emergency evacuation simulations into a BIM-based design environment. Several critical factors for fire evacuation have been reviewed in relevant domain knowledge, which is used to build virtual characters to test in experimental scenarios. The results are analyzed to validate fire emergency evacuation factors, and the feedback knowledge is stored as a knowledge model for further applications

Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices

Special issue "digital twin technology in the AEC industry"

Sustainable building design has become a hot topic over the past decades. Many standards, databases, and tools have been developed for achieving a sustainable building. Not until recently have the importance of structural engineering and its contribution to sustainable building design been full recognised. However, due to the highly fragmented and diversity of knowledge across building and infrastructure domains, there is a lack of approach that can address all the sustainable issues within the structural design. This paper reviews the sustainable design from the perspective of structural engineering: (1) reviewing the current situation; (2) identifying the gaps and difficulties; and (3) making recommendations for future improvements. The strategies and indicators, as well as BIM-enabled methodology, for sustainable structural design (SSD) are also discussed in a holistic way. The results of this investigation show that most of the methods are not doing well in terms of delivering a successful sustainable structural design. It is expected that the future BIM could probably provide such a platform to address these issues

Realization of a two-dimensional checkerboard lattice in monolayer Cu2_2N

Two-dimensional checkerboard lattice, the simplest line-graph lattice, has been intensively studied as a toy model, while material design and synthesis remain elusive. Here, we report theoretical prediction and experimental realization of the checkerboard lattice in monolayer Cu$_2$N. Experimentally, monolayer Cu$_2$N can be realized in the well-known N/Cu(100) and N/Cu(111) systems that were previously mistakenly believed to be insulators. Combined angle-resolved photoemission spectroscopy measurements, first-principles calculations, and tight-binding analysis show that both systems host checkerboard-derived hole pockets near the Fermi level. In addition, monolayer Cu$_2$N has outstanding stability in air and organic solvents, which is crucial for further device applications.Comment: Nano Letters, in pres

Reduction of Dispersion in Ultrasonically-Enhanced Micropacked Beds

Channeling of gas can reduce mass transfer performance in multiphase micropacked-bed reactors. Viscous and capillary forces cause this undesired and often unpredictable phenomenon in systems with catalyst particle sizes of hundreds of micrometers. In this work, we acoustically modify flow in a micropacked-bed reactor to reduce gas channeling by applying high-power sonication at low ultrasonic frequencies (∼40 kHz). Experimental residence time distributions reveal two orders of magnitude reduction in dispersion with ultrasound, allowing for nearly plug-flow behavior at high flow rates in the bed. Sonication appears to partially fluidize the packed-bed under pressurized cocurrent two-phase flow, effectively improving dispersion characteristics.This research was partially funded by the EU project MAPSYN: Microwave, Acoustic and Plasma SYNtheses, under Grant CPIP 309376 of the European Union Seventh Framework Program

Clonal integration promotes the growth of Phragmites australis populations in saline wetlands of the Yellow River Delta

Estuarine wetlands are highly heterogeneous due to strong interactions between freshwater input and seawater intrusion. However, little is known about how clonal plant populations adapt to heterogeneous salinity in soil environments. In the present study, the effects of clonal integration on Phragmites australis populations under salinity heterogeneity were studied using field experiments with 10 treatments in the Yellow River Delta. Clonal integration significantly increased plant height, aboveground biomass, underground biomass, root–shoot ratio, intercellular CO2 concentration, net photosynthetic rate, stomatal conductance, transpiration rate, and stem Na+ content under homogeneous treatment. Under the heterogeneous salt treatment, clonal integration significantly affected total aboveground and underground biomass, photosynthetic traits, and stem Na+ content under different salt gradients. The increase in salt concentration inhibited the physiological activity and growth of P. australis to varying degrees. Compared with the heterogeneous saline environment, clonal integration was more beneficial to P. australis populations in the homogeneous saline habitat. The results of the present study suggest that P. australis prefers homogeneous saline habitats; however, plants can adapt to heterogeneous salinity conditions via clonal integration