Developments in the Built Environment最新文献

Utilising recycled brick aggregate in cementitious mixtures can decrease the overdependency on limited natural resources and improve the sustainability of concrete. This paper presents a potential solution to lower the amount of waste being landfilled and increase the sustainability of 3D concrete printing technology by combining low-carbon ternary blended cement with recycled aggregates. Hence, the effect of incorporating two types of recycled brick aggregate - clay brick (CBA) and engineering brick (EBA) on the properties of 3D printable ternary blended cement were investigated. The natural aggregate in a pre-existing 3D printable blend was substituted by up to 50 wt.-% with two varieties of recycled brick aggregates available throughout Europe. The recycled brick aggregates underwent characterisation to determine their properties. The fresh property evaluation using the green strength test was used to assess the effect of aggregate replacements on the mixture's shape stability. The mechanical performance of mixtures containing CBA and EBA, both cast and 3D printable mixes, was evaluated and compared to that of the control sample. The results indicated that incorporating recycled brick aggregate enhances green strength and Young's modulus significantly. Mechanical strength performance showed significant enhancement when incorporating RBA, which reached up to 67% and 55% for both cast and 3D printing methods, respectively. The suitability of the developed mix formulations for 3D printing was assessed by printing cylindrical objects.

The circular economy (CE) and lean construction (LC) are pivotal for advancing sustainability in the construction industry. CE focuses on minimizing resource use and waste recovery, while LC emphasizes efficiency and waste minimization. Combined, they play a vital role in promoting sustainable development by optimizing resources, minimizing waste generation, and enhancing environmental responsibility. This study presents a novel method to examine the effective combination of these two principles throughout the various stages of the building lifecycle. Through an extensive literature review analysis, we develop a framework that incorporates CE principles across all building stages while leveraging the Lean Project Delivery System (LPDS) to enhance circularity. This approach promotes early collaboration and utilizes smart relational contracts facilitated by blockchain, engaging a diverse team of professionals, including designers, contractors, and specialists. This framework is positioned to redefine industry norms and foster the advancement of sustainable construction methodologies by integrating established principles with innovative technologies.

The presence of inactive components in siliceous and calcareous wastes tends to cause a misunderstanding in their mix design for hydrothermal solidification. The contents of typical reactive elements (i.e. Ca_act, Si_act and Al_act) in raw materials are quickly determined by simulating hydrothermal conditions. A generic mix design framework is proposed, which utilizes the molar ratio of Ca_act/Si_act as the controlling parameter. The framework was demonstrated through a series of studies, which involved the production of granular materials using a mixture of clayey soil with slaked lime, as well as the production of compacted cylinders using a mixture of clayey soil with either slaked lime or calcium carbide slag. Experimental results showed that the hydrothermal samples achieved their maximum strength when the molar ratio of Ca_act/Si_act in raw materials approached the theoretical Ca/Si molar ratio (i.e. 0.83) in tobermorite, provided that the molar ratio of Al_act/(Al_act + Si_act) remained below 21%.

Cementitious composites are porous materials; therefore, controlling their porosity during the design process is crucial to ensuring the quality and strength of the composite. Recently, with the growing urgency to reduce the carbon footprint of cementitious composites, novel admixtures have been introduced as substitutes for cement. Each time a novel admixture is implemented, a research campaign is required to evaluate packing density. Traditional methods used for this purpose are time-consuming, costly, and generate waste. Implementing a waste-free solution using neural networks is a promising alternative. The authors present an analysis of the effectiveness of substituting cement with fly ash and granite powder up to 30% of the cement volume in composites. Additionally, they designed a neural network model to predict the packing density of these mixtures. The practical value of this approach was demonstrated through life cycle assessment analyses.

The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.

This research addresses the persistent challenge of strength degradation in geopolymer-based materials incorporating rubber crumb (RC). An optimization model was developed, focusing on critical variables such as RC grade (particle size), percentage incorporation, and the molarity of NaOH, using slags as alumina-silicate precursors. Response surface methodology (RSM) was employed for experimental design and statistical modelling to predict the strengths and thermal conductivity of the resulting geopolymer. The study meticulously analyzed the influence of each parameter on the performance of RC-based geopolymers to understand their practical implications. The models generated were highly significant, demonstrating high practicability and minimal errors. The optimization revealed that a geopolymer with the highest strength (41.91 MPa) and lowest thermal conductivity (0.504 W/mK) can be achieved using a molarity of 10, grade 20 RC, and 18.5% RC content. This study highlights the potential of optimizing RC-based geopolymer mixes to enhance material performance, promoting the sustainable use of waste tires and advancing the development of high-performance construction materials.

The construction industry is plagued by chronic inefficiencies, such as low productivity, significant waste, cost overruns, and schedule delays. One of the most critical contributors to these issues is fragmented and ineffective planning and control methods. This research aims to address these challenges by developing an innovative integrated multi-level framework for production planning and control that harmonizes various planning methods and control metrics across different schedule levels. Employing the Design Science Research (DSR) methodology, this study systematically develops and evaluates an innovative framework that integrates internal and external project characteristics, utilizing the concept of "functionality" as a primary key for coherent integration. Furthermore, a multi-objective mathematical model is incorporated to clarify the framework's components and optimize the number of suggested planning methods and control metrics at each scheduling level, aiming to balance the functional requirements of project teams with the practicality of implementation.

The application of the framework to a renovation case study demonstrates its practicality and strategic value, effectively addressing real-world project management challenges. Moreover, industry and academic evaluations highlight its potential to significantly improve construction project management practices.

This research introduces an extensive database aggregating 54 experimental programs with 804 test specimens and 45 input parameters, investigating the implications of chloride-induced corrosion on the deteriorated mechanical response of corroded reinforced concrete beams. Several machine learning models are explored to determine the highest performing predictor for five key response variables – the residual ultimate moment capacity, residual capacity factor, yield load, yield displacement, and the ultimate displacement. Three existing analytical approaches are included for comparison to verify the efficacy of the trained statistical models. The optimized machine learning models significantly outperformed conventional analytical methods and achieved high levels of predictive accuracy. Ensemble tree-based learning algorithms, namely gradient-boosting regression trees and random forests, consistently produced the best predictions. Finally, the top-performing models are aggregated into a Python-based application that allows users to input new data and predict the mechanical response of a corroded beam failing in bending.

Alkali-activated slag cementitious material (AASCM) is a promising green building material that can effectively reduce carbon dioxide emission. Nano-silica (NS) can effectively improve the mechanical properties of cement-based materials and reduce their shrinkage; however, the effect of AASCM is unknown. Therefore, the rheology, workability, compressive strength, flexural strength, shrinkage and microstructure of NS-AASCM are investigated in this study, and the effects of the water-binder ratio (w/b) and NS content are considered. Results show that the rheological characteristics and workability of the NS-AASCM increases with the w/b and decreases as the NS content increases. Under the w/b of 0.55, 2% NS content increases the compressive and flexural strengths by 42.1% and 36.6%, respectively. The shrinkage of the AASCM increases with the w/b and NS content. The lower the w/b, the more significant is the effect of the NS content on the NS-AASCM shrinkage growth rate. SEM results show that the appropriate NS content improves the compactness of the AASCM gel matrix.

Real utilisation patterns often only become apparent when a building is put into operation. Since planning is therefore based on several assumptions, the potential of building technology is often limited. Post-occupancy evaluations offer the potential to adapt operation to real usage patterns and draw conclusions for future planning, thereby improving performance. Although solution approaches are available in the literature, this potential is currently hardly being utilised. A survey in the construction industry is therefore used to compare market requirements with existing solution concepts. The results emphasise the relevance of auto-commissioning, contractual agreements on performance evaluation and the coupling of digital twins with post-occupancy evaluations. A stronger anchoring of the control industry in BIM is proving to be necessary for better cross-disciplinary collaboration between all parties involved. Even though the focus of this study is on lighting, the most important findings are in principle transferable to other trades.