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The construction industry generates significant timber waste, particularly from formwork systems, posing environmental and economic challenges. To promote resource recycling and sustainable construction, this study investigates the feasibility of utilizing recycled composite wood joists, fabricated from construction waste timber, polypropylene, polyester thread, and fibre composite bands, as the secondary joists in concrete formwork systems. Through theoretical analysis and on-site experiments, the load-bearing performance of the composite joists was evaluated under equivalent loads corresponding to concrete slabs of varying thicknesses. The results demonstrated that the deflection trends of the joists aligned closely with theoretical predictions, with a maximum deflection of 5.65 mm recorded under 4th load mode. The composite joists met the deformation standards (≤4 mm) for slabs up to 200 mm thickness, which can meet the relevant specifications for formwork construction. The study concludes that the recycled composite wood joist is a viable, eco-friendly alternative for secondary joists in formwork systems, contributing to sustainable construction practices by repurposing construction waste.

With the aim of predicting the environmental vibrations induced by an elevated high-speed railway, a machine learning method was developed by combining a random forest algorithm and Bayesian optimization, using a dataset from on-site experiments. When it comes to achieving a rapid and effective prediction of environmental vibrations, there is little research on comparisons between and verifications of different algorithms, and none on the parameter tuning and optimization of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on a bridge, and then the environmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine learning algorithms using linear regression, support vector machine, and random forest are developed using an experimental database, and their prediction performance is discussed. Finally, two optimization models for the hyperparameter set of the random forest algorithm are further compared. The results show that the integrated random forest algorithm has a higher accuracy in predicting environmental vibrations than linear regression and the support vector machine; the Bayesian optimization has an excellent performance and a high efficiency in achieving efficient and in-depth optimization of parameters and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.

Recent years have seen the emergence of local air pollutant monitoring networks that feature close proximity to urban activities, higher requirement for temporal granularity, and improvisions in equipment hardware and installation conditions. These networks are intended for the pertinent monitoring and improvement of urban air quality, but potential technical issues may undermine their ability to serve such purposes. This study utilizes a minute-granularity network in a university campus, and designs and conducts a series of experiments on how it performs under practical scenarios, including response to sudden environment change, reflection of multi-scale influencing factors, usability of different baseline stations, and ability to detect local emission events. Statistical and signal-processing technics are applied for understanding these experiments. The results indicate the source of complexity in such networks, the preferred temporal granularity for capturing different temporal patterns, the necessary reserved time for mobile stations, and the sensor location requirement for monitoring local emission events etc. In practical terms, these results provide a large amount of information on the specific capabilities and limitations of a near-surface, high-granularity monitoring network in the urban environment, and what to consider and to expect as a designer or user of such a network. In scientific terms, it is a strong reminder of the significance of numerous uncertainty issues in similar empirical studies.

There is currently no mature calculation theory to accurately predict the settlement of high-fill subgrade. This paper developed an intelligent monitoring system to accurately predict the settlement of high-fill subgrade based on on-site experiments, and the back-propagation (BP) neural network model was used to predict the settlement of high-fill subgrade. The results show that multiple data preprocessing methods built into intelligent systems can automatically generate multi-point and correlation curves, and the system can identify and distinguish various influencing factors to improve the accuracy and reliability of monitoring data. There will be a certain initial settlement of subgrade in the initial stage after filling construction is completed, and the settlement rate at this stage is relatively fast. Afterwards, the soil enters a rapid consolidation stage, and the settlement rate of subgrade gradually slows down. Finally, the filling soil consolidation becomes stable, and the rate of subgrade settlement enters a relatively stable stage. In addition, the BP neural network model is a good method for predicting the settlement of high-fill subgrade. The research findings can provide inspiration for developing an intelligent monitoring system to accurately predict the settlement of high-fill subgrade.

Machine-induced vibrations represent, for several reasons, a crucial design issue for industrial buildings. At the early design stage, special attention is thus required for the static and dynamic performance assessment of the load-bearing members, given that they should optimally withstand ordinary design loads but also potentially severe machinery operations. The knowledge and reliable description of the input vibration source is a key step, similarly to a reliable description of the structural system, to verify. However, such a kind of detailing is often unavailable and results in a series of simplified calculation assumptions. In this paper, a case-study eyewear factory built in 2019 is investigated. Its layout takes the form of a two-story, two-span (2 × 14.6 m) precast concrete frame (poor customer/designer communication on the final equipment resulted in various non-isolated computer numerical control (CNC) vertical machines mounted on the inter-story floor, that started to suffer from pronounced resonance issues. Following past experience, this paper investigates the validity of a coupled experimental–numerical method that could be used for efficient assessment predictive studies. Based on on-site experiments with Micro Electro-Mechanical Systems (MEMS) accelerometers mounted on the floor and on the machine (spindle included), the most unfavorable machine-induced vibration sources and operational conditions are first characterized. The experimental outcomes are thus used to derive a synthetized signal that is integrated in efficient one-bay finite element (FE) numerical model of the floor, in which the machine–structure interaction can be taken into account. The predictability of marked resonance issues is thus emphasized, with a focus on potential and possible limits of FE methods characterized by an increasing level of detailing and computational cost.

In order to investigate the operation performance of ground-coupled heat-pump (GCHP) system, an analytical simulation model of GCHP system on short time-step basis and a computer program based on this model to predict system operating parameters are developed in this study. Besides, detailed on-site experiments on GCHP test rig installed in a temperate region of China are carried out. The temperature distributions of borehole as well as ground around borehole at different depths are evaluated. Operation parameters of GCHP system such as circulating water temperature, heat rejection into ground and system power consumption when the system operated in intermittent and continuous modes are investigated. The accuracy of proposed simulation model is validated by experimental data. The advantage of GCHP technology in energy efficiency over other conventional air-conditioning systems is proved to be obvious and the performance of GCHP system is found to be affected by its operation modes.

1 Ecological and agronomic research suggests that increased crop diversity in species-poor intensive systems may improve their provision of ecosystem services. Such general predictions can have critical importance for worldwide food production and agricultural practice but are largely untested at higher levels of diversity. 2 We propose new methodology for the design and analysis of experiments to quantify diversity-function relationships. Our methodology can quantify the relative strength of inter-specific interactions that contribute to a functional response, and can disentangle the separate contributions of species richness and relative abundance. 3 Applying our methodology to data from a common experiment at 28 European sites, we show that the above-ground biomass of four-species mixtures (two legumes and two grasses) in intensive grassland systems was consistently greater than that expected from monoculture performance, even at high productivity levels. The magnitude of this effect generally resulted in transgressive overyielding. 4 A combined analysis of first-year results across sites showed that the additional performance of mixtures was driven by the number and strength of pairwise inter-specific interactions and the evenness of the community. In general, all pairwise interactions contributed equally to the additional performance of mixtures; the grass-grass and legume-legume interactions were as strong as those between grasses and legumes. 5 The combined analysis across geographical and temporal scales in our study provides a generality of interpretation of our results that would not have been possible from individual site analyses or experimentation at a single site. 6 Our four-species agricultural grassland communities have proved a simple yet relevant model system for experimentation and development of methodology in diversity-function research. Our study establishes that principles derived from biodiversity research in extensive, semi-natural grassland systems are applicable in intensively managed grasslands with agricultural plant species.

The value and direction angle of the perpendicularity error of the thrust surface and bearing bush in hydrostatic thrust bearings directly affect the motion accuracy. In this paper, a rapid onsite measurement method and evaluation model for the value and direction angle of the perpendicularity error of the thrust surface and bearing bush in hydrostatic thrust bearings are presented. The method is validated by comparing experimental measurements with those obtained using a CMM. An analysis of the perpendicularity errors before and after correction shows that at different speeds (10 rpm, 30 rpm, and 50 rpm), the errors in the mean axial and tilt displacements of the thrust bearing are reduced by 68.41% and 35.44%, respectively.

We describe six experiments set up at four regional locations in Great Britain, in 1993 and 1994, to examine the impact of control treatments on bracken and associated vegetation. Present discussion is limited to the effects of treatments on bracken frond variables (density, length and dry mass). These variables would be used by a land manager to judge the extent of infestation and the efficacy of control methods. Results of statistical analyses are reported for the period 1994 to 1998, inclusive. The treatments showed great variability in effectiveness between both sites and years. Great inter-regional differences were seen, but stands at sites within a short distance of each other also varied in their response to treatment. Meso- and micro-climatic differences are suggested as possible causes, together with stand growth phase and genetic effects. The most effective treatments in the short-term were found to be combinations of cutting and herbicide spraying, applied once. Annual cutting usually gave a better result in the longer term. All treatments had greatly improved effects when combined with a follow-up application of herbicide several years after commencement. A number of recommendations are given for management, such as best methods for short- and long-term results. Systematic monitoring is urged as changes in frond density, for example, may reveal the extent of the problem for control at a particular site.

In recent years, designing in existing environments has been consistently emphasized in community planning. However, practicing such on-site design is not easy for designers, because the current technical conditions do not allow virtual design objects into real environments for 3D visualization and interaction. Thus, designers’ intuitive design perceptions, accurate design judgments, and convenient design decisions are hardly supported. This paper explores the possibilities of using mixed reality (MR) technology to improve designers’ on-site design experiences in community planning. For this, we introduced an MR design support system (MR-DSS) for the interactive on-site 3D visualization of virtual design objects. With the MR-DSS, we performed a design experiment with sixteen participants in a typical on-site design scene of community planning. The results showed that the MR technology could provide designers with intuitive design perceptions, accurate design judgments, and convenient design decisions, thus effectively improving their on-site design experiences.