Architecture, Structures and Construction最新文献

With the rise of interest in digital fabrication of reinforced concrete structures, a great number of structural concrete designs that depart from standard prismatic shapes are being suggested. This has prompted an exploration of steel reinforcement strategies that are alternative to the classical deformed or “ribbed” rebars. One such is to cut internal reinforcement from steel plates using a waterjet cutting machine. Advantages of automated waterjet cutting steel reinforcement include high precision and accuracy, and minimal expense for increasing the complexity of (2D) reinforcement layouts. However, it is not known how the application of ribbing patterns along the cut edge of reinforcing bars affects the steel–concrete bond. This work conducts experimental pullout tests of waterjet-cut steel plate reinforcement with three different ribbing patterns and compares the bond strength with equivalent classic rebars. Two of the tested geometries averaged within 90–91% of the pull-out force of conventional rebar, demonstrating viability of this alternative reinforcement method.

Abstract

This paper describes the 1:1 scale application of Robotic Plaster Spraying (RPS), a novel, adaptive thin-layer printing technique, using cementitious base coat plaster, realized in a construction setting. In this technique, the print layers are vertical unlike most 3DCP processes. The goal is to explore the applicability and scalability of this spray-based printing technique. In this study, RPS is combined with an augmented interactive design setup, the Interactive Robotic Plastering (IRoP), which allows users to design directly on the construction site, taking the building structure, as-built state of the on-going fabrication and the material behavior into consideration. The experimental setup is an on-site robotic system that consists of a robotic arm mounted on a semi-mobile vertical axis with an integrated, automated pumping and adaptive spraying setup that is equipped with a depth camera. The user interaction is enabled by a controller-based interaction system, interactive design tools, and an augmented reality interface. The paper presents the challenges and the workflow that is needed to work with a complex material system on-site to produce bespoke plasterwork. The workflow includes an interactive design procedure, localization on-site, process control and a data collection method that enables predicting the behavior of complex-to-simulate cementitious material. The results demonstrate the applicability and scalability of the adaptive thin-layer printing technique and address the challenges, such as maintaining material continuity and working with unpredictable material behavior during the fabrication process.

Contaminated soil materials are often unsuitable for engineering construction works due to high content of impurities which inhibits development of mechanical strength and durability properties. This study was therefore necessary to make available empirical evidence revealing the consequences of crude petroleum pollution on the mechanical characteristics of materials made of concrete and determine its effect on civil engineering works. The study involved collection of contaminated soil sample from the Kolomani oil-well in Bauchi State, Nigeria and laboratory evaluation performed to evaluate the engineering possessions of concrete formed from contaminated soil sample. A calculated ingredient at a proportion of water to cement (W/C) set at 0.5, a mix proportion of 1:1.8:2.7 was used with the fine aggregate content replaced partially with crude oil contaminated soil materials (COCM) from 0–40%. The obtained laboratory results showed rise in compressive strength property as COCM fraction increases with the optimal response of 16.36 N/mm² derived at 20% replacement. The experimental results was further subjected to statistical analysis using one-way ANOVA and F-statistics to test the source of variation for the geotechnical properties. Multiple Linear Regression (MLR) and correlation statistics were then used to establish relationship between mix ratios and the geotechnical properties. The results signifies good performance with R-squared of 82.81. The benefits derived from this work would enhance production of sustainable concrete works which can be applicable in large scale for tile production.

Previous studies have investigated the behaviour of prestressed stayed steel columns with a view to developing design guidelines. As most of the modern standards move towards calibrated safety factors, there is a need to explore the design expectations of these structures from a probabilistic perspective. This has not been studied in the literature till now, perhaps due to lack of clarity on underlying uncertainties, failure models and reliability levels. In this paper, the relevant reliability levels were studied to highlight the critical modes for such structures. A sensitivity analysis was performed on the random variables to investigate their impact on the model output. The reliability levels found through appropriate analysis were then compared with target reliabilities to calibrate partial safety factors to be used in the design of these structures. A range of safety factor values were found depending on the conditions adopted in the calibration and target reliability studies.

Despite the impact of decision-making in the design of buildings and places, there is limited understanding concerning how decisions are best made, or how these should be evaluated and optimised. Additionally, technological advancements have increased human-to-machine interactions, altering existing decision-making processes. By understanding how novel technologies affect decisions, it motivates the development of the process, tools, and metrics. The aim of this paper is to investigate, quantify, and rank the relative importance of the decision-making factors contributing to the design of building and urban projects. A survey was conducted to gain an insight of stakeholders’ perceptions as to which are the influencing factors affecting decision-making processes in the design of buildings and places. Ten distinct factors were generated , of which, four were ranked as highly important for all stakeholder types, namely: Potential for Dynamic Operation, Thoroughness, Recency of Tools and Control. This study provides a new means to evaluate performance of decision-making processes, when these are undertaken, by developing and applying a quantitative data-driven, evidence-based methodological framework. The recipients of the findings will be the urban planners, designers, and academics who are interested in improving existing approaches in design and final decision outcomes utilising novel technologies.

Abstract

Maximizing indoor comfort while minimizing energy costs has been a challenging problem for building management systems. This problem is significantly exacerbated for large public buildings with varying occupancy levels. A practical approach to measure occupants’ comfort has been to evaluate the indoor thermal comfort using Fanger’s Predictive Mean Vote (PMV) model, parameterized by the ambient temperature and Relative Humidity (RH). Such an approach is, however, one dimensional and does not consider other possible sources of discomfort like indoor air quality. Interestingly, the ambient temperature and RH, in addition to thermal comfort, also influence the amount of emissions from indoor furnishings, which is a prime source of indoor air quality degradation. Taking this into account, in this paper, we adapt the definition of comfort to include indoor air quality as well. Since occupancy levels, occupants’ activities and outdoor temperature vary with time, one way to achieve desired comfort goals is to continuously adapt the settings of Heating, Ventilation, and Air Conditioning (HVAC) units in buildings. Such a continuous adaptation results in significant energy costs, especially in geographical locations where outdoor temperatures can be significantly higher/lower than desired indoor temperatures. In this context, we propose a location-aware multi-objective optimization model for indoor comfort and energy cost management. We combine conflicting objectives—improving air quality and thermal comforts, and minimizing energy cost—to determine cost-driven, comfort-driven and Pareto optimal solutions using Multi-Objective Genetic Algorithm (MOGA). The proposed model is envisioned to enable building operators to determine suitable temperature and RH as per occupants’ requirement. The solution can be personalized based on the building structure and macro- and micro-location parameters. To ease configuration and customization of our model based on building- and geography-specific settings, we also present a MATLAB-based GUI that operators can leverage to understand the comfort-cost trade-off for buildings.

Detecting the presence of cracks and identifying their severity are crucial tasks for determining the structural health of a concrete building. In this study, we develop a two-stage automated method based on the You Only Look Once (YOLOv5) deep learning framework for the identification, localization, and quantification of cracks in the concrete structures. In the first stage, cracks are identified and localized using bounding boxes, while in the second stage, the length of cracks and, therefore, the damage severity are determined. The developed deep learning model is trained using 4500 annotated images from a total of 40000 images of size 227 × 227 pixel, which are obtained from an open-source dataset collected at various campus buildings of Middle East Technical University (METU). The concept of transfer learning (i.e., pre-trained weights) is used for the training, which drastically reduces the training time. The detection and localization accuracy of this model is measured in terms of the average precision, average recall, and F1-score. The YOLOv5 model achieves the mean average precision (mAP_0.5) of 95.02%. A ResNet model is also developed just to capture the supremacy of the YOLOv5 model. The proposed method can help in identifying structural anomalies through real-time monitoring that must be urgently repaired and thus can be used in high-quality civil infrastructure monitoring systems.