Developments in the Built Environment最新文献

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.

3D printed polymeric reinforcement has been found able to improve the ductility of cementitious materials. However, due to the hydrophobic nature of commonly used 3D printing polymers, the bonding strength between the 3D printed polymers and cementitious matrix is extremely weak, which potentially hinders the mechanical performance of the reinforced composites. This work aims to improve the bonding properties by applying surface modifications on the 3D printed reinforcement, and eventually enhance the mechanical performance of the reinforced cementitious composites. Three types of surface coatings ingredients: epoxy resin (EP), sand sprinkled epoxy (SA) and short steel fibers sprinkled epoxy (SF) were used. Pull-out experiments are performed to study the bonding properties of the 3D printed reinforcement with different coatings. Then, uniaxial tensile and four-point-bending experiments are used to investigate the mechanical performance of the reinforced cementitious composites. A lattice type numerical model is applied to simulate the pull-out and tensile tests. The pull-out experiments indicate that the SA and SF reinforcement achieved approximately two times higher bonding strength than the uncoated and EP reinforcement. The tensile and flexural results suggest that the cementitious composites with SA and SF reinforcement achieved significantly better ductility (manifested by strain-hardening and deflection-hardening behavior) than the composites with uncoated and EP reinforcement. The numerical simulation results highly agree with the experimental findings, and further confirmed that the improved reinforcement-mortar bonding strength is the determinative factor that enhanced the composites mechanical performance. The findings of this work suggest that the sand and steel fiber surface coatings can effectively enhance the ductility of cementitious composites reinforced by 3D printed polymers.

Ultra-high-performance concrete (UHPC) emerged as a promising material for modern construction, offering unparalleled strength, durability, and versatility. However, there is limited research regarding UHPC's response to fire, prompting urgent investigation and analysis. This paper presents an experimental study focused on evaluating the effect of different types of fibers on the fire performance of UHPC circular columns. The fiber types considered in this study included steel fibers (SF), a hybrid mix of steel and polypropylene fibers (SPPF), and polyvinyl alcohol fibers (PVAF). Additionally, a control column was cast without the inclusion of any fibers. All UHPC specimens were subjected to standard fire exposure, following the ASTM E119 fire test. The results indicated a substantial resistance to spalling with the incorporation of fibers. Moreover, the column featuring PVAF exhibited the lowest internal temperature readings among the columns. Columns reinforced with SF and SPPF exhibited uniform spalling on all sides.

Per-person ventilation rates were assessed from data measured in 322 UK schools throughout the Autumn term, 2023. It was found that the overall mean ventilation rate was 5.3 L s⁻¹ p⁻¹; rising to 6.8 L s⁻¹ p⁻¹ during warmer weather and falling to 3.8 L s⁻¹ p⁻¹ during colder weather. Daily mean CO₂ levels recorded in the majority of schools consistently fell below the 1500 ppm threshold within government school ventilation guidance but the contribution of relatively few very high CO₂ levels in some classrooms shows the need for targeted improvement and highlights the value of large-scale datasets. Exceedance of the 1500 ppm threshold rose to approximately 20% when the daily mean outdoor temperature fell to around 5 °C, indicating a barrier to ventilation. Controlling for outdoor temperature as a covariate, analysis of the CO₂ measurements indicated schools in more-deprived regions, state-funded schools (relative to private), and secondary schools (relative to primary) exhibited statistically-significant higher CO₂ concentration. Reassuringly, these findings remained unchanged when the analysis was deployed on estimated per-person ventilation rates; except that this enhanced analysis accounted for the greater CO₂ production associated with older children and demonstrated that ventilation rates were not significantly different between primary and secondary schools.

This study aims to comprehensively investigate the evolution of microstructure, mechanical strength, and their correlation in alkali-activated slag (AAS) mortars, designed for application in the immobilization of liquid radioactive waste, under accelerated carbonation conditions (1% CO₂, 20 °C and 60% RH). To gain insights into the underlying microstructural changes, CO₂ uptake and decalcification of C-A-S-H were analyzed using TGA/DSC and EDS. The pore structure of AASs was systematically assessed across nano- to macro-scales, employing N₂-adsorption, MIP, and SEM segmentation. Generally, carbonation led to a decrease in total porosity, primarily attributed to the reduction in meso-macropore volume. However, the pore size distribution of AAS exhibited a complex alteration over varying carbonation durations. Carbonation significantly reduced flexural strength, whereas its effect on compressive strength was comparatively milder. Notably, an evident linear correlation emerged between porosity and compressive strength in both reference and carbonated AASs.