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Concrete-infilled hollow flange cold-formed steel (CF-HFCFS) beams have gained attention in the construction practices owing to many benefits in terms of their structural performances and applicability. The concrete infill ensures better structural performance by restraining the buckling instabilities of thin-walled cold-formed steel elements. However, the shear strength characteristics of CF-HFCFS are not systematically explored yet and hence there is a lack of understanding on the shear strength characteristics of CF-HFCFS beams. Therefore, in this research, shear characteristics were investigated through numerical studies by establishing and analysing three-dimensional finite element (FE) models of CF-HFCFS beams. The developed FE models were verified against the experimental data in terms of failure modes, ultimate shear capacities, and load-displacement characteristics. Then a series of parametric analyses were carried out to investigate the shear behaviour of CF-HFCFS beams against the effects of geometrical (steel thickness, beam depth) and mechanical (yield strength of steel and compressive strength of concrete) properties to further verify the shear characteristics of CF-HFCFS. Lightweight normal and lightweight high strength concrete materials were considered as infill. Also, the influence of the concrete infill on the ultimate shear capacity of the CF-HFCFS beams was evaluated through parametric studies. The ultimate shear capacities were compared against the already available design provisions. Consequently, based on the data established through of parametric analyses, modified design provisions are developed to estimate the ultimate shear capacity of CF-HFCFS beams.

Current legislation focuses on reducing the operational carbon impact of buildings. However, the production of materials used in construction generates a considerable amount of carbon, known as embodied carbon, that accounts for a sizeable fraction of the environmental impact of a building during its lifecycle. We present a newly developed tool, pycab, which calculates the embodied carbon of a building directly at the design stage and compares it to the Royal Institute of British Architects (RIBA) 2030 Climate Challenge Target Benchmarks. As input, the tool uses standard Industry Foundation Classes (IFC) files that can be produced directly from existing Building Information Modelling (BIM) software. The pycab tool enables industry professionals to make design stage decisions that reduce the embodied carbon impact of their projects. This research demonstrates one of the many potential uses that digital tools can have in reducing the environmental impact of the construction industry.

Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness.

This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were gathered to train machine learning models including support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB). The cross-validation with 10 folds has been conducted in the training process to avoid over-fitting. The optimal hyperparameter combinations for each machine learning model were found during the hyperparameter tuning process and four performance indicators were used to evaluate the performance of the trained models. The comparison results suggest that all four implemented machine learning models reliably predict the shear capacity of both stainless steel lipped channel sections and carbon steel LiteSteel sections while the implemented SVR algorithm is found to be the best performing model. Moreover, it is shown that the implemented machine learning models exceed the prediction accuracy of the available design equations in estimating the shear capacity of steel channel sections.

A product-service system (PSS) has many engineering characteristics (ECs), their design is a critical work in PSS planning, which has an important influence on the cost and quality of PSS. How to design a reasonable PSS-ECs scheme, and evaluate its life cycle cost (LCC) is a challenging task. Aiming at the PSS-ECs design for LCC, this paper proposes a new PSS design method, it first treats and models the design of PSS-ECs as a customer requirements-based constraint satisfaction problem (CSP) for finding an initial set of satisfied PSS-ECs schemes,and then it evaluates these schemes based on Bayesian network (BN)-based LCC estimation model for finding an optimal scheme as a solution. Constructing a BN describing the uncertain relationships between PSS-ECs and LCC is the core of this research. By combining existing R&D data and expert experience, Bayesian estimation and arithmetic averaging are used to estimate the conditional probability in BN. Take a subway bogie and its maintenance service in a Chinese company as an example to verify the proposed method. The results show that the proposed method can effectively solve the problem of PSS-ECs design for LCC, it also shows that this method has positive significance in realizing engineering knowledge consolidation, assisting designers in exploring design space, and improving the rationality of design decisions.

In this paper, the global dynamics of a hanging fluid conveying cantilevered pipe with a concentrated mass attached at the free end is investigated. The problem is of interest not only for engineering applications, but also because it displays interesting and often surprising dynamical behaviour. The widely used nonlinear models based on the transverse motion of the pipe are not able to accurately capture the dynamical behaviour of the system at very high flow velocities. Thus, a high-dimensional geometrically-exact model is developed for the first time, utilising Hamilton’s principle together with the Galerkin modal decomposition technique. Extensive numerical simulations are conducted to investigate the influence of key system parameters. It is shown that at sufficiently high flow velocities past the first instability (Hopf bifurcation), the system undergoes multiple bifurcations with extremely large oscillation amplitudes and rotations, beyond the validity of third-order nonlinear models proposed to-date. In the presence of an additional tip mass, quasi-periodic and chaotic motions are observed; additionally, it is shown that for such cases an exact model is absolutely essential for capturing the pipe dynamics even at relatively small flow velocities beyond the first instability.

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.

Microbial community assembly mechanisms are pivotal for understanding the ecological functions of microorganisms in biogeochemical cycling in Earth's ecosystems, yet rarely investigated in the context of deep terrestrial ecology. Here, the microbial communities in the production waters collected from water injection wells and oil production wells across eight oil reservoirs throughout northern China were determined and analyzed by proportional distribution analysis and null model analysis. A 'core' microbiota consisting of three bacterial genera, including Arcobacter, Pseudomonas and Acinetobacter, and eight archaeal genera, including Archaeoglobus, Methanobacterium, Methanothermobacter, unclassified Methanobacteriaceae, Methanomethylovorans, Methanoculleus, Methanosaeta and Methanolinea, was found to be present in all production water samples. Canonical correlation analysis reflected that the core archaea were significantly influenced by temperature and reservoir depth, while the core bacteria were affected by the combined impact of the core archaea and environmental factors. Thermodynamic calculations indicate that bioenergetic constraints are the driving force that governs the enrichment of two core archaeal guilds, aceticlastic methanogens versus hydrogenotrophic methanogens, in low- and high-temperature oil reservoirs, respectively. Collectively, our study indicates that microbial community structures in wells of oil reservoirs are structured by the thermodynamic window of opportunity, through which the core archaeal communities are accommodated directly followed by the deterministic recruiting of core bacterial genera, and then the stochastic selection of some other microbial members from local environments. Our study enhances the understanding of the microbial assembly mechanism in deep terrestrial habitats. Meanwhile, our findings will support the development of functional microbiota used for bioremediation and bioaugmentation in microbial enhanced oil recovery.

Integration of energy recovery section with thermal desalination systems improves their performance from thermodynamics, economics, and environmental viewpoints. This is because it significantly reduces input energy, heat transfer area, and capital cost requirements. Above all, the system outlet streams can achieve thermal equilibrium with the environment by supplying heat for useful preheating purposes thus reducing the environmental impacts. The plate heat exchangers are generally employed for this purpose as preheaters. The current paper presents a comprehensive investigation and optimization of these heat exchangers for thermal desalination systems applications. An experimentally validated numerical model employing Normalized Sensitivity Analysis and Genetic Algorithm based cost optimization is developed to investigate their performance at assorted operating conditions. The analysis showed that the heat transfer coefficient, pressure drop, and outlet water cost were improved by an increase in feed flow rate. However, with an increased flow rate, the comprehensive output parameter (h/ΔP) decreased due to the high degree increase in pressure drop. Moreover, an increase in the chevron angle reduced the heat transfer coefficient, pressure drop, and water cost. Finally, the optimization lowered the heat transfer area by ~79.5%, capital investment by ~62%, and the outlet cost of the cold stream by ~15.7%. The operational cost is increased due to the increased pressure drop but the overall impact is beneficial as Ctotal of equipment is reduced by ~52.7%.

Cross-laminated timber (CLT) is an innovative construction material that has brought advantages over traditional wood structures, reducing cost and lead time of buildings in recent years; yet CLT benefits primarily from offsite construction methods instead of automation or safety, while keeping the human onsite. The few advancements in automation for CLT panels have been in the implementation of dedicated CNC machines. Nevertheless, using CNC machines for machining CLT panels have disadvantages like clamping batches of massive panels with individual profiles, lacking the flexibility to access all acute machining angles, and struggling with the extraction of dust while the cutting spindle moves through large tight spaces. These disadvantages can be overcome with industrial robots’ help, which the construction industry has not been traditionally favorable on their application, giving then the research gap in this study. This paper explores the introduction of a robotic cell for the machining of cross-laminated timber panels. The robotic cell is designed using 3D modeling and validated through motion simulation in a virtual environment. The proposed cell design is based on a minimum viable product and compared against a minimum throughput benchmarked on the Canadian market. This study aims to research the feasibility of CLT’s automated machining by providing clear production characteristics of the designed robotic cell, such as material and tool utilization rates, lead time, or production efficiency.

This research investigated fresh and hardened properties of lightweight self-compacting concrete (LWSCC) incorporated with oil palm shell (OPS) and fly ash (FA). Fresh concrete properties including passing ability, filling ability and segregation resistance were assessed. The properties fulfilled EFNARC guidelines. Incorporation of FA improved fresh properties, particularly filling ability, with the slump flow value increased from 665 mm to 730 mm. As for hardened properties, OPS-aggregate based LWSCC mixes achieved compressive strength of range 18–38 MPa at 28-day age while the splitting tensile strength was in the range of 1.6 to 2.8 MPa. SEM analyses showed good bonding in the interfacial transition zones (ITZ). Micro-pores of OPS were filled by cement hydration products and thus ITZ was enhanced. LWSCC incorporated with OPS, a renewable resource from agricultural waste, and with partial FA replacement, is potentially a sustainable alternative construction material.

An ultra-high sensitivity label-free optical fiber biosensor for inactivated Staphylococcus aureus (S. aureus) detection is proposed and investigated in this study, with additional advantages of robust and stability compared to traditional tapered fiber structure. The proposed fiber biosensor is based on a tapered singlemode- no core-singlemode fiber coupler (SNSFC) structure, where the no core fiber was tapered to small diameter (taper-waist diameter of about 10 μm) and functionalized with the pig immunoglobulin G (IgG) antibody for detection of S. aureus. The measured maximum wavelength shift of the sensor for an S. aureus concentration of 7 × 101 CFU/mL (colony forming unit per milliliter) is 2.04 nm, which is equivalent to a limit of detection (LOD) of 3.1 CFU/mL (a highest LOD reported so far for optical fiber biosensors), considering the maximum wavelength variation of the sensor in phosphate buffered saline (PBS) is ±0.03 nm over 40 min, where 3 times of maximum wavelength variation (3 × 0.03 = 0.09 nm) is defined as measurement limit. The response time of the developed fiber sensor is less than 30 min. The ultra-sensitive biosensor has potential to be widely applied to various areas such as disease, medical diagnostics and food safety inspection.

Borates are promising candidates as dielectric substrate materials in low temperature cofired ceramics technology (LTCC) due to their relative low sintering temperatures and relative permittivities compared to their counterparts. However, synthesizing borates having single-phase is still challenging because of the volatility and hydrophilicity of boron resources. In this work, a compositional design was utilized to synthesize single-phase LiBGeO4 ceramics over a broad temperature range from 600 to 840 °C. Radio-frequency dielectric behaviours featured a strong temperature dependence, especially at high temperatures (>400 °C), which is related to the thermally activated polarizations. LiBGeO4 ceramic sintered at 820 °C has optimum microwave dielectric properties with the relative permittivity (er) of 6.28, a quality factor (Q × f) of 21,620 GHz, and a temperature coefficient of resonance frequency (τf) of -88.7 ppm/°C. LiBGeO4 also showed chemical inertness when cofired with silver (Ag), provided an evidence for its utilization in LTCC technology. Overall, this work provides a strategy for facile synthesis of phase pure borates, via the proposed two-step process to obtain stable boron resources.

In corporate boardrooms around the world, climate change has quickly risen to become a major issue, matching public concern. Recently, corporate management has encountered stakeholder pressure to disclose more information about their carbon profile and their plans to improve it. They have also been challenged to find the appropriate strategy for carbon disclosures, requiring an understanding of the costs and benefits of both carbon improvement initiatives and the reporting of them. Using a unique data set that contains firms listed on the FTSE 350 index on the London Stock Exchange market from 2009 to 2015, we apply the event study method to examine market reaction to carbon disclosures. The results show that investors respond significantly negatively to carbon disclosure announcements via Carbon Disclosure Project (CDP) of FTSE 350 firms. Moreover, for firms working in carbon-intensive industries, investors react to carbon disclosure announcements in a more significantly negative way compared with the main sample. We also find that the study’s main findings are driven by the smaller FTSE 350 firms. Furthermore, a subsample of observations for the financial crisis period of 2007–2008 was analyzed to explore the examined relationship during the crisis. In contrast, a significant positive market reaction to carbon disclosure was found for the 2007–2008 crisis period. Our study’s findings offer fresh insight and updated policy implications for investors, management and sustainability institutions. We recommend management accompanies their carbon disclosures with more explicit statements of reasons for carbon initiatives and the benefits arising from them.

The demand for highly structurally efficient stainless steel is limited to a certain extent by its high initial cost. Therefore, the utilisation of material to the optimum possible level is important. In achieving this, further consideration should be given to enhance the design rules where beneficial effects such as pronounced strain hardening in stainless steel should be taken into account in the design process. In addition to that, a thorough understanding of the structural behaviour of stainless steel sections is also required. However, the shear behaviour and capacity of cold-formed stainless steel lipped channel beams (LCBs) have not been thoroughly investigated previously. Therefore, experimental and detailed finite element (FE) modelling were undertaken to investigate the shear behaviour and strength of stainless steel LCBs. A comprehensive parametric study was also conducted by developing 100 FE models. From the results, the available post-buckling strength in slender stainless steel LCBs was highlighted. Furthermore, the beneficial strength increment due to the strain hardening effect of stainless steel, particularly for compact LCBs in shear, was investigated. Comparisons indicated that current EN1993-1-4 and direct strength method (DSM) shear design rules are too conservative in particularly for compact sections. Thus, existing shear design rules were modified to enhance the overall prediction accuracy for stainless steel LCBs while attention was given to capture the available inelastic reserve capacity.

This paper proposes a new approach to creating Building Information (BIM) models of existing buildings from digitized images. This automatic approach is based on three main steps. The first involves extracting the useful information automatically from rasterized plans by using image processing techniques that include segmentation, filtering, dilation, erosion, and contour detection. This information feeds the knowledge base of an expert system for BIM model generation. In the second step, using the knowledge base of the expert system, the information required to inform the BIM model can be deduced. The range of information thus obtainable can be extended beyond the examples given. The paper concludes with a discussion of the final stage: the automatic generation of an Industry Foundation Classes (IFC) information model with all the desired geometric, physical and technical information. This can be accomplished by using one of the available open-source application program interfaces (APIs). This stage is currently work-in-progress and will be the subject of a future publication.

A facile method for fabricating intelligent microwave absorber of vapor grown carbon fibers/Polydimethylsiloxane-epoxy resin shape memory composites (VGCFs/PDMS-SMEP) was proposed to deliver intelligently tunable and broadband microwave absorption performance. The maximal absorption intensity was regulated by varying the deformation of the composites driven by the superior shape memory property of SMEP, where practical the minimum reflection loss (RLmin) reaches −55.7 dB at 16.0 GHz with the thickness of 2.0 mm. The effective absorption bandwidth (EAB) reached 9.8 GHz, which covered the whole applied frequency range (8.2–18.0 GHz). The intelligent microwave absorption performance of the sample was attributed to robust conductive loss and dielectric loss enhanced by the dipole relaxations and multi-reflections. Thus, VGCFs/PDMS-SMEP composites serves as the key that really opens up opportunity for the application as flexible, shape memory and tunable high performance broadband microwave absorption absorber in frontiers such as wearable electronic devices, chips protection, stealth technology and information security.

Learning factories are a rising and promising environment for engineering education, training, and research. Practical interest in learning factories has peaked in recent years, attracting the worldwide attention of scholars and practitioners. This study conducts a scientometric review of the global research published since 1995 on learning factories, through co-citation, keyword and clustering analysis. A total of 367 journal papers and conference proceedings from the Scopus database were analyzed, to distinguish key publications and main research trends. By performing a mapping exercise, a detailed analysis of the learning factories’ literature enables academic support for future research. The provided results are used to identify the main areas of contribution of learning factories research and understand the potential gaps in current research.

The Element-Free Galerkin method (EFG) is a prominent member of the meshfree methods family. In this work, EFG is utilised to simulate the orthogonal cutting process of unidirectional composites. The mathematical model is derived from the weak form of the momentum conservation equation with frictional contact constraints based on penalty method. Spatial discretisation using moving least squares shape functions are used. The onset and progression of damage are predicted by two stress-based failure criteria. Full Newton Raphson solver is used to solve the non-linear system equations iteratively. Numerical experiments investigating the effect of rake angle and fibre orientation are conducted. Cutting forces are compared against experiments and finite element simulations available in literature. Simulations show that the meshfree model is capable of predicting cutting forces as a function of the fibre orientation. Sensitivity analysis is conducted to investigate the effect of important meshfree parameters such as the domain of influence and weight function on forces. One of the strongest advantages of the proposed model is the simple and automatic set up process, as meshing for domain discretisation is not required.

Additive manufacturing (AM) provides many benefits such as reduced manufacturing lead times, streamlined supply chains, part consolidation, structural optimisation and improved buy-to-fly ratios. Barriers to adoption include high material and processing costs, low build rates, isotropic material properties, and variable processing conditions. Currently AM polymer parts are far less expensive to manufacture than AM metal parts, therefore improving the properties of polymer parts is highly desirable.This paper introduces a design methodology used to integrate continuous reinforcement into AM polymer parts with the aim of improving their mechanical properties. The method is validated with the design and testing of three case studies, a pulley housing, hook and universal joint used to demonstrate the applicability of the method for tensile, bending and torsion loading types respectively.Physical testing showed that it was possible to improve the strength of parts by over 4000%, elongation to failure by over 2000% and stiffness by approximately 200%. In addition a method of integrating condition monitoring capabilities into the parts was demonstrated.An analysis of the specific strength of the parts suggests that the reinforced parts are comparable to aluminium alloys, suggesting that in some cases AM polymer composite parts could supplant more costly metal parts. Keywords: Carbon fibre, Polymer-matrix composites, Mechanical properties, Finite element analysis, Additive manufacturing, Condition monitoring

The problem that has been haunting for long manufacturing industries (automotive, aerospace, domestic appliance and electronics) is the lubrication. Most processes are impossible without lubrication, otherwise wear and galling will occur. On the other hand, many lubricants are toxic and some lubricants are even flammable, and therefore the use of lubricants is a big environment, safety and health concern.\ud This paper demonstrates how innovative self-lubricated coating such as MoS2/Titanium composite coatings can be used to solve these problems and are suitable for the specific requirements with characteristics such as low frictional coefficient, high wear resistance and low cost.\ud For example, demand coatings such as MoS2/Titanium composite coatings is increasing, therefore forming and cutting process can be performed “dry” without use additional lubricants, that is, the oil based lubricants can be eliminated from processes.\ud The benefits of MoS2/Titanium composite coatings are both ecological and economical. The ecological side is obvious. The economical benefits can be obtained from the elimination of the direct costs of lubricants and the application apparatus, from longer tool life and less tool maintenance, from improved quality of machined parts as well as from the indirect costs of parts cleaning and waste disposal.\ud Industrial results from forming and cutting processes will be presented and understanding analysis will be performed. Parameters such as life-time, cutting force, surface finishing… will be correlated to laboratory friction and wear tests as well as microstructure observations.