Developments in the Built Environment最新文献

Innovative solutions are needed to improve the durability of concrete structures in marine environment. Bacteria-based agents (BAS) and crystalline admixtures (CA) are explored as healing agents to enhance chloride resistance and prevent corrosion. Healing of 100 μm and 300 μm wide cracks was investigated, in combination with two conditioning methods. Either the samples were subjected to wet/dry cycles for 3 months before exposure (“healed”), or they were directly exposed to artificial seawater after crack creation (“unhealed”). After 12 months of submersion, BAS reduced chloride ingress even in the presence of cracks but showed limitations in preventing corrosion in cracked samples. In contrast, the CA series demonstrated a reduction in chloride ingress in both uncracked and cracked concrete and effectively prevented reinforcement corrosion in healed samples and samples with cracks of 100 μm. This highlights the potential of customized self-healing solutions to improve concrete durability in marine environments.

To investigate the influence of volume fraction of lightweight aggregate concrete on dynamic tensile strength and size effect, theoretical derivation and microscopic numerical simulation are combined in this study, and a dynamic hybrid fracture cohesive zone constitutive model for lightweight aggregate concrete to characterize the tensile behavior considering strain rate at microscopic scale. The research reveals that, for geometrically similar specimens of various sizes, the direct tensile strength with different aggregate volume fractions exhibits differing degrees of improvement with increasing strain rates. The tensile strength gradually decreases as volume fraction increases. The established coupling function incorporating strain rate, specimen size, and aggregate volume fraction accurately characterizes the dynamic tensile strength variation. In addition, a theoretical framework is proposed to provide a microscopic understanding of the static-dynamic size effect. This framework facilitates the estimation of the dynamic tensile strength under specific volume fraction, specimen size and strain rate conditions.

Unsafe hoisting operations have been consistently associated with numerous safety incidents involving tower cranes. Currently, the predominant measures to mitigate these operations center around comprehensive training and education, emphasizing standardized protocols prior to hoisting activities. Despite concerted efforts in this direction, a conspicuous research gap persists in early-warning mechanisms during the construction phase. This paper aims to address this gap by proposing an innovative early-warning methodology, inspired by the principles of digital twin and knowledge graph. We firstly introduce a digital twin framework designed to mirror the real-time operational status of the tower crane. This framework enables the immediate detection of deviations or infractions as they occur. Subsequently, we develop a knowledge graph capable of promptly identifying unsafe hoisting operations by leveraging real-time data obtained from the digital twin. To validate the efficacy of our proposed methodology, we construct a scaled-down replica of a tower crane and establish a tailored digital twin system. The findings of a series of experimental trials prominently underscore the system's capability to generate timely alerts in response to unsafe hoisting operations while maintaining an impressively low rate of false alarms.

Offline programming (OLP) is a mainstream approach for controlling assembly robots at construction sites. However, existing methods are tailored to specific assembly tasks and workflows, and thus lack flexibility. Additionally, the emerging large language model (LLM)-based OLP cannot effectively handle the code logic of robot programming. Thus, this paper addresses the question: How can robot control programs be generated effectively and accurately for diverse construction assembly tasks using LLM techniques? This paper describes a closed user-on-the-loop control framework for construction assembly robots based on LLM techniques. A hierarchical strategy to generate robot control programs is proposed to logically integrate code generation at high and low levels. Additionally, customized application programming interfaces and a chain of action are combined to enhance the LLM's understanding of assembly action logic. An assembly task set was designed to evaluate the feasibility and reliability of the proposed approach. The results show that the proposed approach (1) is widely applicable to diverse assembly tasks, and (2) can improve the quality of the generated code by decreasing the number of errors. Our approach facilitates the automation of construction assembly tasks by simplifying the robot control process.

In this work, sustainable cement clinkers and ecofriendly cementitious materials (ECMs) were prepared with iron ore tailings (IOTs). Alternative raw meals with IOTs were sintered to cement clinkers by conventional sintering processes. The results of X-ray diffraction (XRD), scanning electron microscopy (SEM) and mechanical tests suggested that the cement clinker within 10 wt% IOTs had better quality than those without IOTs. In addition, the hydration and hydration products of the IOT-based cement were analyzed via XRD and SEM. Before preparing ECMs, IOTs were pretreated with a 100 mesh Tyler screen to remove silt and clay. Then, the pretreated IOTs and whole IOTs partly instead of fine aggregates, together with IOT-based cement were used to produce ECMs. The ratio of water-binder and compressive strength properties of the ECMs were investigated. It is suggested that the pretreated IOTs sand can be used as much as 60 wt%, while the amount of whole IOTs sand must be limited to 20 wt% before the performance dramatically decrease. These findings suggest that disposal of a high volume of Yeshan IOTs in sustainable construction building materials has feasibility and operational significance.

It is important to clarify the uniaxial compressive mechanical properties and fracture mechanism of recycled aggregate concrete (RAC) from a mesoscale perspective for its further application in engineering. In this study, the cohesive zone model (CZM) based on the Benzeggagh–Kenane (B–K) criterion was used to simulate the RAC's complex fracture behavior. Meanwhile, the effects of cohesive element parameters and mechanical properties of mesoscale components on macroscopic mechanical properties and damage modes of RAC were investigated. The results show that the CZM based on the B–K criterion can be used to characterize the whole fracture process of RAC. The RAC's compressive strength has an exponential relationship with the shear strengths of the mortars as well as the ITZs and is quadratically related to the logarithm of cohesive element stiffness. The RAC's damage morphology is more sensitive to the change of element stiffness, Mode II fracture energy and hybrid fracture energy ratio.

Digital twins need efficient methodologies to design maintenance strategies for decision-making purposes. Recently, a methodology coupling computational simulation and multiobjective evolutionary algorithms has been proposed for developing maintenance strategies consisting in assigning times for preventive maintenance activities and designing the layout of components of a system, minimizing the unavailability of the system and the strategy cost.

Here, surrogate assisted evolutionary algorithms (SAEAs) enhance the multiobjective optimization and improve the drawback of the computational cost of the maintenance strategy assessment based on discrete simulation. Several Kriging surrogates were tested.

Two industrial test cases are handled in the experimental section, where the methodology succeed in obtaining nondominated designs improving previous benchmarks, and enhancing state-of-the-art multiobjective optimizers, with up to an order of magnitude in terms of the number of fitness function evaluations. Results show that using multiobjective SAEAs in the development of optimal maintenance strategies could foster and improve digital twins operations.

This paper introduces an alternative approach for detecting cavities in reinforced concrete walls using Ground Penetrating Radar (GPR) A-scan data. GPR, leveraging electromagnetic waves, is extensively applied for cavity detection within structures. The nature of electromagnetic waves, significantly influenced by reflective media and attenuating through them, requires specialized analysis methods for data interpretation. Traditional methods often involve identifying and eliminating overlapping reflection patterns or adjusting signal magnitude at specific depths to isolate peak signals from the target object's surface, which can be subjective and complex. To overcome these challenges, this study proposes quantitatively assessing the presence of cavities by analyzing the integral area of A-scan data within suspected ranges. Observations indicate a substantial difference in reflection patterns between areas with and without cavities, showcasing the potential of this approach for quantitative cavity detection. This approach offers a more objective and quantitative basis for identifying cavities in reinforced concrete structures.

Digital Twin (DT) concept is used in different domains and industries, including the building industry, as it has physical and digital assets with the help of Building Information Modeling (BIM). Technologies and methodologies constantly enrich the building industry because the amount of data generated during different building stages is considerable and has a tremendous effect on the lifecycle of a building. Previous research underscores the importance of seamlessly exchanging information between physical and digital assets within a comprehensive framework, particularly emphasizing the integration of BIM data with various systems to enhance efficiency and prevent information loss. Despite advancements in technologies, challenges persist in optimizing methods for integrating BIM data into DT frameworks, including ensuring interoperability, scalability, and real-time monitor and control. This study addresses this research gap by proposing a comprehensive platform that integrates the DT concept with IoT and BIM technologies. The platform is developed in five main stages: 1) acquiring electronic data of the building from the laser scanner, 2) developing a Wi-Fi IoT module and BIM data for physical assets and digital replica, 3) constructing the DT elements of the platform, 4) performing data analysis 5) implementing thermal comfort prediction models. Two machine learning models (Facebook prophet, NeuralProphet) are implemented to predict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operation. A case study demonstrates the practical application of the proposed framework. The case study involves a real building where the platform is implemented to monitor and control indoor environments. By utilizing predefined data in BIM models, the platform ensures data accuracy, consistency, and usability. The case outputs reveal that Neuralprophet provides good prediction results.