Search

Cracks in concrete structures are one of the most important indicators of structural damage, and it is a necessity to detect and measure cracks for ensuring safety and integrity of concrete structures. The widely practised approach in inspecting the structures is by performing visual inspections followed by manual estimation of crack widths. This approach is not only time-consuming, laborious, and time-intensive but also prone to subjective errors and inefficient. To address these issues, we propose a novel deep learning framework for detecting cracks and then estimating crack widths in concrete surface images. Our framework handles both small- and large-sized images and provides a prediction of crack width at locations specified by the user. The proposed framework uses Attention Recurrent Residual U-Net (Attention R2U-Net) with Random Forest regressor to predict crack width with the mean prediction error of ±0.31 mm for crack widths varying from 0 to 8.95 mm and produces the lowest absolute maximum error of 1.3 mm. Our model has a coefficient of determination ( R2) of 0.91, showing a non-linear mapping function with low prediction errors. We compare our model with a combination of four other segmentation models and regression models. Our proposed model has superior performance compared to other models, and one can easily adopt our framework to a variety of Structural Health Monitoring applications using Internet of Things sensors.

The environmental impact of traditional Ordinary Portland Cement (OPC) concrete is a significant problem that requires urgent solutions in the construction industry. The development of geopolymer concrete is one of the most significant breakthroughs in the process of replacing OPC concrete. Through comprehensive experimental and numerical analyses, this study investigates the structural behaviours of large-scale steel reinforced geopolymer concrete beams (GCBs) made from low-calcium fly ash and ground granulated blast furnace slag (GGBFS). Firstly, small-scale experiments were carried out to investigate the effects of water/binder and activator/binder ratios on the mechanical properties of geopolymer concrete (e.g. elastic modulus, compressive strength, direct tensile strength). The experimental results show that the mechanical properties of geopolymer concrete increases with increasing the activator/binder ratio and decreasing the water/binder ratio. Secondly, three groups of GCBs with different steel reinforcement ratios (D1-0.41%, D2-0.75%, and D3-1.5%) were made and four-point bending tests were conducted. The same mix proportion (water/binder of 0.45 and activator/binder of 0.08) with the compressive strength of 39.1 MPa, elastic modulus of 32.0 GPa, and the direct tensile strength of 3.06 MPa, was used for the three groups. The obtained moment–curvature results, which consists of three distinct stages (linear elastic, tension cracking of GCBs, and steel yielding), show that the three groups (D1-D3) behave in a ductile manner. Moreover, the moment capacity of GCBs increases when the steel reinforcement ratio increases (D1-21.5 kNm, D2-44.2 kNm and D3-83.6 kNm). Finally, nonlinear, three-dimensional finite element (FE) analysis based on the damage plasticity constitutive law was developed to capture and validate the structural behaviour of GCBs from the experiments. Numerical results indicate that the developed FE models accurately capture the structural behaviours (moment–curvature and cracking behaviour) of GCBs. The discrepancies between the numerical and experimental moment–curvature results are from 1 to 5% for the tensional cracking and yielding points. Therefore, the developed FE models can be used as an effective tool for the further development and design of geopolymer concrete structures.

PurposeBuilding information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.Design/methodology/approachThis paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.FindingsFour domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.Originality/valueThis paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

Retrofitting concrete with fiber-reinforced polymer (FRP) wrap has been considered as an effective method to compensate for the loss of the compressive strength of the concrete at an elevated temperature. This paper presents a study on the axial compressive behavior of heat-damaged steel fiber-reinforced concrete strengthened with carbon FRP (CFRP) wraps. To this effect, steel fibers were added to two concrete mixtures with volume fractions of 0.5% and 1%. The effect of full and partial wrapping with CFRP composites on the compressive behavior of the heated concrete was examined. A stress–strain model was also proposed to predict the behavior of CFRP-confined post heated steel fiber-reinforced concrete. It was found that heated concrete wrapped with CFRP layers exhibits a higher compressive strength, peak axial strain, maximum volumetric strain, toughness and relative energy absorption than that of unwrapped concrete. The results also showed that the exposure of concrete to the heat leads to a slight decrease in the ascending and descending slopes of the axial stress–strain relationship. The comparison between the model predictions and experimental results revealed that the proposed model accurately predicts the axial stress-axial strain and axial stress-lateral strain of the heated concrete. These results are promising and demonstrate the benefits of full and partial FRP wrapping in the development of high-performance structural members subjected to an elevated temperature.

Contrary to conventional buildings, the structural robustness of modular high-rise buildings has not been studied due to the lack of the numerical and experimental studies and design guides. To gai...

Based on the notion of Stark units, we present a new approach that obtains refinements of log-algebraic identities for Anderson $t$-modules. As a consequence, we use our techniques to recover many earlier results and prove stronger results in some cases. Further, we devise a direct and conceptual way to get logarithmic interpretations for multiple zeta values in positive characteristic. This generalizes the work of Anderson and Thakur for Carlitz zeta values.

We study positive characteristic multiple zeta values associated to general curves over $\mathbb F_q$ together with an $\mathbb F_q$-rational point $\infty$ as introduced by Thakur. For the case of the projective line these values were defined as analogues of classical multiple zeta values. In the present paper we first establish a general non-commutative factorization of exponential series associated to certain lattices of rank one. Next we introduce universal families of multiple zeta values of Thakur and show that they are Eulerian in full generality. In particular, we prove a conjecture of Lara Rodriguez and Thakur arXiv:2003.12910. One of the main ingredients of the proofs is the notion of L-series in Tate algebras introduced by the third author arXiv:1107.4511 in 2012.

Biological structures possess excellent damage tolerance, which makes them attractive for ballistic protection applications. This paper develops a finite element modelling framework to investigate the performance of several biological structures that are most relevant for ballistic protection, including nacre, conch, fish scales, and crustacean exoskeleton. Finite element simulations were conducted to determine the geometric parameters of the bio-inspired structures that can survive projectile impact. The performances of the bio-inspired panels were benchmarked against a monolithic panel with the same 4.5 mm overall thickness and projectile impact condition. It was found that the biomimetic panels that were considered possessed better multi-hit resistant capabilities compared to the selected monolithic panel. Certain configurations arrested a fragment simulating projectile with an initial impact velocity of 500 m/s, which was similar to the performance of the monolithic panel.

The use of cameras for safety monitoring, progress tracking, and site security has grown significantly on construction and civil infrastructure sites over the past decade. Localization of ...

This research focuses on types of substrate materials (rice husk, coconut fiber, sand), the rate of media mix between coconut fiber, rice, and concentrations of Thiamine HCl (vitamin B1) on the growth of Rainbow Vinegar (Beta vulgaris subsp. Vulgaris). In the studies of nutritional absorption and metal toxicity in the roof, it is essential to growing plants without technical damage. The results showed that Hoagland solution combined coconut fiber, sand, and Thiamine HCl (vitamin B1) suitable for Beta vulgaris subsp. Vulgaris. The Rainbow grows remarkably from 20 days to 30 days for a faster, cleaner, and cultivating soil environment. The method presented here provides. Vulgaris to obtain a healthy plant having a well-developed root system with many lateral roots.

Many alternative approaches for 3D object detection using a singular camera have been studied instead of leveraging high-precision 3D LiDAR sensors incurring a prohibitive cost. Recently, we proposed a novel approach for 3D object detection by employing a ground plane model that utilizes geometric constraints named GAC3D to improve the results of the deep-based detector. GAC3D adopts an adaptive depth convolution to replace the traditional 2D convolution to deal with the divergent context of the image’s feature, leading to a significant improvement in both training convergence and testing accuracy on the KITTI 3D object detection benchmark. This article presents an alternative architecture named eGAC3D that adopts a revised depth adaptive convolution with variant guidance to improve detection accuracy. Additionally, eGAC3D utilizes the pixel adaptive convolution to leverage the depth map to guide our model for detection heads instead of using an external depth estimator like other methods leading to a significant reduction of time inference. The experimental results on the KITTI benchmark show that our eGAC3D outperforms not only our previous GAC3D but also many existing monocular methods in terms of accuracy and inference time. Moreover, we deployed and optimized the proposed eGAC3D framework on an embedded platform with a low-cost GPU. To the best of the authors’ knowledge, we are the first to develop a monocular 3D detection framework on embedded devices. The experimental results on Jetson Xavier NX demonstrate that our proposed method can achieve nearly real-time performance with appropriate accuracy even with the modest hardware resource.

Modular construction is considered as a game-changing technology since it offers faster construction, safer manufacturing, better quality control, and lower environmental impacts compared with the traditional onsite construction. These benefits can be maximised in high-rise buildings due to their inherently topological modular form and the increased number of repeatable modules. However, current applications of modular construction for high-rise buildings are very limited due to the lack of strong structural systems and joining techniques to ensure structural integrity, overall stability, and robustness of an entirely modular building. In addition, the unavailability of design guidelines also inhibits the construction industry in implementing such technology. With recent advancements in structural systems and materials, there is great potential for real world applications of modular construction in high-rise buildings. This paper presents a critical review of recent innovations in modular construction technology for high-rise buildings with an emphasis on structural systems, joining techniques, progressive collapse and structural robustness. The developments of design codes for modular construction are also discussed. The paper concludes by highlighting the technical challenges that hinder the widespread adoption of modular construction, and proposing potential solutions for future research. This review paper is expected to be a complete reference for experts, researchers and professionals in this field of study.

The protection of critical infrastructure, including government buildings, airports, religious buildings, military buildings and military vehicles, which are at risk to blast loads, has become important due to increasing terrorist activities in recent years. Sacrificial cladding systems based on negative Poisson’s ratio core topologies have recently received more attention as a protective technology due to its excellent energy absorption capability. In this study, field blast tests were performed on metallic re-entrant honeycomb-cored sacrificial cladding systems as protective structures for steel plate structures. This study focused on the near-field blast loading conditions where liquid Nitromethane (NM) spherical charges were detonated in close proximity to the main structure. Two 6 mm thick mild steel plates and two steel plates protected with re-entrant honeycomb-cored sacrificial cladding systems were among the specimens tested. The proposed auxetic cladding system was fabricated from aluminium sheets using a novel in-house built folding machine. Numerical simulations were conducted utilising LS-DYNA software and the Blast Impact Impulse Model (BIIM). The results obtained from the numerical simulations are in good agreement with the experimental results. It was found that the deformation pattern of the sacrificial auxetic cladding system varies with the intensity of the blast loading, and there is a limit at which the proposed protective system ceases to effectively absorb the applied blast loading. The variation of negative Poisson’s ratio of the system with blast loading was studied. It was found that the auxetic cladding system could become a solid projectile leading to damage amplification for very close-range blast loads due to rapid densification of the auxetic core. The proposed cladding systems with narrow re-entrant angles performed well under blast loads due to relatively low stiffness of the panels. Finally, the optimisation study was performed for the protective system. Overall, the experimental and numerical results assure that auxetic-based cladding systems are suitable for applications requiring blast protection such as armoured vehicles and critical physical infrastructure but need to be carefully designed for the given blast threat to prevent overloading of the protected structures.

With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, we construct a data set of crack/non-crack concrete structures, comprising 32,704 training patches, 2074 validation patches, and 6032 test patches. We evaluate the performance of our approach using 15 state-of-the-art convolutional neural network models in terms of number of parameters required to train the models, area under the curve, and inference time. Our approach provides over 95% accuracy and over 87% precision in detecting the cracks for most of the convolutional neural network models. We also show that our approach outperforms existing models in literature in terms of accuracy and inference time. The best performance in terms of area under the curve was achieved by visual geometry group-16 model (area under the curve = 0.9805) and best inference time was provided by AlexNet (0.32 s per image in size of 256 × 256 × 3). Our evaluation shows that deeper convolutional neural network models have higher detection accuracies; however, they also require more parameters and have higher inference time. We believe that this study would act as a benchmark for real-time, automated crack detection for condition assessment of infrastructure.

Computer vision has been gaining interest in a wide range of research areas in recent years, from medical to industrial robotics. The architecture, engineering and construction and facility management sector ranks as one of the most intensive fields where vision-based systems/methods are used to facilitate decision making processes during the construction phase. Construction sites make efficient monitoring extremely tedious and difficult due to clutter and disorder. Extensive research has been carried out to investigate the potential to utilise computer vision for assisting on-site managerial tasks. This paper reviews studies on computer vision in the past decade, with a focus on state-of-the-art methods in a typical vision-based scheme, and discusses challenges associated with their application. This research aims to guide practitioners to successfully find suitable approaches for a particular project.

As a developing country in the context of globalization, Vietnam is experiencing a boom in its economy, characterized by a rapid rate of urbanization nationwide. Together with its benefits, this process also puts pressure on the environment, among which the increasing generation of construction and demolition waste (CDW) is an urgent issue. In this study, the authors conducted an in-depth investigation of various aspects in the generation and management of CDW in Vietnam. Firstly, part of the overall picture of CDW management in Vietnam was revealed in detail from the perspective of demolition contractors, one of the important stakeholders in the field. Their insights provide valuable information on current situation, practice, and attitude towards CDW recycling. Secondly, this paper reports the generation rate of different categories of materials from demolition sites of building structures in Hanoi, Vietnam, with the main focus on crushed concrete and crushed brick (CC–CB) and steel as they make up the majority of the generated waste. In order to achieve two mentioned goals, close contact was constantly kept with a key collaborator – a renowned demolition contractor in Hanoi throughout the investigation. The collaborator provided proper introduction to enable interviews with various other contractors, together with information of buildings being dismantled.

2019/20 Australia's bushfire season (Black Summer fires) occurred during a period of record breaking temperatures and extremely low rainfall. To understand the impact of these climatic values we conducted a preliminary analysis of the 2019/20 bushfire season and compared it with the fire seasons between March 2000 and March 2020 in the states of New South Wales (NSW), Victoria, and South Australia (SA). Forest and fire management in Australia were asked to provide data on the number of fires, burned area, life and house loss, as well as weather conditions. By March 2020 Black Summer fires burnt almost 19 million hectares, destroyed over 3,000 houses, and killed 33 people. Data showed that they were unprecedented in terms of impact on all areas. A number of mega-fires occurred in NSW resulting in more burned area than in any fire season during the last 20 years. One of them was the largest recorded forest fire in Australian history. Victoria had a season with the highest number of fires, area burned, and second highest numbers of houses lost for the same period. SA had the highest number of houses lost in the last 20 years. Black Summer fires confirmed existing trends of impact categories during the last two decades for NSW and Victoria. It showed that the smoke from the bushfires may be a significant concern in the future for the global community, as it travels to other countries and continents. Based on preliminary data, it will take many years to restore the economy and infrastructure in impacted areas, and to recover animal and vegetation biodiversity.

Hard turning is a potential machining process to replace for grinding process due to large advantages such as material removal rate, good surface integrity, and friendly environment. This process is also used to process discontinuous surface parts. This study focuses on analyzing the effect of cutting parameters on surface roughness and flank wear after interrupted hard turning of SKD11 steel using CBN inserts. The effect of cutting parameters and interacted between them on the surface roughness and the flank wear were analyzed by using the full factorial design having the central trials. The results investigated that the feed rate is the most significant parameter affecting on the surface roughness in the machining process and the cutting speed strongly affects on the flank wear in the interrupted hard turning of SKD11 steel using high CBN inserts. The results of ANOVA analysis for the surface roughness and the flank wear indicated that the need to investigate and use the curve model to describe the effect of the cutting parameters on the surface roughness and the flank wear.

Solar cooling technologies have been proven to have great potential for energy saving during cooling season. Meanwhile, glass has become one of the primary structural materials used in building construction since the middle of the 20th century. Although common glass adds to the aesthetic appeal of a building, it has serious drawbacks, such as creating heat traps, preventing natural ventilation and causing glare. Highly glazed facades would cause unwanted heat transmission from the ambient, which must be extracted to outside using an air-conditioning system. Internal heat resulting from facade configurations can be responsible for up to 45% of a building’s cooling requirements. A facade integrated solar cooling system can simultaneously improve building’s energy efficiency, utilise solar energy and still maintain a high level of architectural and aesthetic quality. This investigation presents a consistent approach for optimising and comparing facade integrated solar cooling systems in terms of technical and financial performance. Four systems (a vapour compression cycle (VCC) chiller driven by semi-transparent photovoltaics (STPV) arrays, a single-stage absorption chiller, an adsorption chiller and a vapour compression chiller coupled with organic Rankine cycle (ORC) driven by evacuated tube solar collectors) were assessed and compared with a conventional electric vapour compression chiller. The systems investigated were modelled in TRNSYS and the models were applied to predict performance parameters in various climate zones (seven cities) in Australia. The solar fraction (SF) and unit cooling cost (UCC) were the two parameters applied to quantify the technical and financial aspects of each solar cooling system in seven cities in Australia. It was found that among the systems investigated, the VCC chiller with STPV system has the highest SF (100% except in Darwin) and lowest UCC ($0.21 kWhr−1) for all seven cities in Australia. In general, due to the grid as a virtual storage, ORC-VCC system has higher SF (40% and 50%) and lower UCC (5% and 10%) compared with adsorption and absorption chiller respectively in subtropical and temperate climate zones.

Re-entrant and honeycomb cellular structures have shown potential for mitigating the effects of extreme loadings such as those imposed by impacts and near-range air blast. However, these cellular geometries can buckle locally and collapse in the immediate vicinity of the loading, which can limit their effectiveness as a protective element. These deficiencies can be addressed by mimicking alternate, naturally occurring, cellular structures, including that of the porcupine quill, which is studied here. The quill possesses several distinct features that effectively counteract buckling and bending, and minimise weight. This study mimics several structural features of the quill to develop a novel cellular design for counteracting air blast loads such as those associated with detonations of high explosives. The performance of the bio-mimetic structure is benchmarked against traditional hexagonal and re-entrant designs, which have been documented in the archival literature. The quill-inspired structure offers more design freedom than the traditional cellular geometries. By iteratively mimicking several of the structural features of the porcupine quill, an optimal balance between local buckling and collapse can be realised, which minimises the reaction on the target below and maximises energy dissipation.

Lightweight concrete foam is mainly used as a filling for sandwich panels for insulation of buildings. Surfactants are chemical admixtures that play an important role in stabilising the air pores in fresh concrete foam before stiffening. This study investigates the effects of surfactants on the microstructure and pore characteristics of concrete foam analysed by X-ray microtomography. The formation of larger pores due to poor stability of bubbles in the concrete foam is directly related to a substantial reduction of compressive strength. Anionic (negatively charged) surfactants produce a stable aqueous foam. However, in the presence of cement particles, the majority of anionic surfactants adsorb on positively charged sites of cement particles. As the result of considerable migration of surfactants from the air–liquid interface of bubbles, the concrete foam is destabilised. Therefore, a surfactant that can generate a stable foam (with water only) may not be able to generate a stable concrete foam. A combination of an anionic and a non-ionic (neutral) surfactant reduced the maximum pore diameter from 1·84 mm to 1·49 mm and increased strength by 25% compared to the concrete foam stabilised by anionic surfactants alone.

The ability to monitor and track the interactions between construction equipment and workers can lead to creating a safer and more productive work environment. Most recent studies employ computer vision and deep learning techniques, which rely on the size and quality of the training datasets for optimal performance. However, preparation of large datasets with high quality annotations remains a manual and time-consuming process. To overcome this challenge, this study presents a framework for synthetically generating large and accurately annotated images. The contribution of this paper is manifold: First, a method is developed using a game engine, which employs domain randomization (DR) to produce large labelled datasets for excavator pose estimation. Second, a state-of-the-art deep learning architecture based on high representation network is adapted and modified for excavator pose estimation. This model is trained on synthetically generated datasets and its performance is evaluated. The results reveal that the model trained on synthetic data can yield comparable results to the model trained on real images of excavators. This demonstrates the effectiveness of utilizing synthetic datasets for complex vision tasks such as equipment pose estimation. The study concludes by highlighting directions for further work in synthetic data studies in construction.

Multiples zeta values and alternating multiple zeta values in positive characteristic were introduced by Thakur and Harada as analogues of classical multiple zeta values of Euler and Euler sums. In this paper we determine all linear relations among alternating multiple zeta values and settle the main goals of these theories. As a consequence we completely establish Zagier-Hoffman's conjectures in positive characteristic formulated by Todd and Thakur which predict the dimension and an explicit basis of the span of multiple zeta values of Thakur of fixed weight., Comment: 41 pages

Background In acute ischemic stroke (AIS) caused by intracranial large vessel occlusion, rescue intracranial stenting (RIS) has been recently a treatment option to achieve recanalization in patients with the failure of mechanical thrombectomy (MT). Nevertheless, there are few studies supporting this beneficial treatment in two cerebral circulations. We aimed to analyze whether the use of RIS would improve prognosis “non-poor” of patients at 3 months. Methods and Findings This was a interventional, single-arm study in patients with AIS who were treated with rescue stenting at Can Tho S.I.S hospital. Inclusion criteria consisted of: evidence of intracranial large vessel occlusion, absence of intracranial hemorrhage and severe stenosis or reocclusion after MT. Tandem lesion, loss to follow-up after discharge and a severe or fatal combined illness before AIS were excluded. The primary outcome was the “non-poor” outcome rate at 3 months and postprocedural symptomatic intracerebral hemorrhage (sICH). The study is registered with ClinicalTrials.gov, NCT04986774.Between August 2019 and May 2021, 85 eligible patients were comprised of 82 (96.5%) successful recanalization and 4 (4.7%) sICH. “Non-poor” outcome comprising of good (mRS 0 - ≤ 2) and fair (mRS 3). “Non-poor” outcome at 3 months occurred 47 (55.3%), in which there were 35 (41.2%) good outcome. DAPT was associated with new infarcts (RR = 0.1; 95%CI 0.01 - 0.7, NNT = 2) and sICH (RR = 0.1; 95%CI 0.01 - 0.9; NNT = 2). MRI 3 Tesla evaluated diagnostic occlusive lesions with sensitivity (Se) = 98.5%, positive Likelihood Ratio (LR+) = 3.5. The pc-ASPECTS < 6 points was associated with poor outcome (RR = 2.1, 95% CI 1.2- 3.7). Many predictors from demographic, history, time onset, dysphagia, imaging of MRI 3 Tesla, preprocedure, procedure and postprocedure were demonstrated the influence on poor outcome after RIS significantly (all RR > 1, all p < 0.05). The main limitations of the study was conducted in a single center, these results from clinical symptoms to imaging of MRI 3 Tesla could not only be influenced by selection bias but also not generalize to other countries in Asia. Conclusions The RISIS trial suggests that RIS could be an important alternative and additional treatment afterfailureMT despite low proportion of postprocedural sICH. Trial registration Clinicaltrials.gov, Identifier:NCT04986774.

Ultra-high strength concrete (UHSC) have received increasing research attention due to its superior performance. However, many of the existing UHSC mixes may require special curing methods such as heat or pressure. In addition, some mixes contain unusual materials, such as silica sand and ground quartz in order to improve the inner structure of the concrete. Therefore, they seem to be uneconomic due to the high cost of manufacture as well as complexity, which is making UHSC out of reach for most constructions in practice. As a solution for these issues, the present work proposes three concrete mix designs with grades of 130, 150 and 170 MPa. The mixtures are designed for manufacturing cost-cutting and advancing the practicality of UHSC by using normal raw materials (including cement, fly ash, silica fume, natural sand and basalt aggregate) and simple curing condition with ambient temperature. Moreover, the proposed mixes are also compared with existing mixes of recent researches in terms of superplasticizers dosage and the presence of specially graded aggregates to demonstrate their advancement.

The monitoring of air quality compliance requires the use of Federal Reference Methods (FRM)/Federal Equivalent Methods (FEM); nevertheless, the validity and reliability of low-cost sensors deserve attention due to their affordability and accessibility. This review examines the methodologies of previous studies to characterise the performance of low-cost air quality sensors and to identify the influential factors in sensor evaluation experiments. The data on four statistical measures (Correlation of Determination, r2; Root Mean Square Error, RMSE; Mean Normalised Bias, MNB; and Coefficient of Variation, CV) and details about five methodological factors in experimental design (environmental setting, reference instrument, regression model, pollutant attribute, and sensor original equipment manufacturer (OEM) specification) were extracted from a total of 112 primary articles for a detailed analysis. The results of the analysis suggested that low-cost air quality sensors exhibited improved r2 and RMSE in the experiments with stable environmental settings, in the comparison against non-designated reference instruments, or in the analysis where advanced regression models were used to adjust the sensor readings. However, the pollutant attribute and sensor OEM specification had inconclusive effects on r2 and RMSE due to contradictory results and lack of sufficient data. MNB and CV, two measures that US EPA recommends to determine the suitable application tier of air quality sensors, varied significantly among published experiments due to the discrepancy in experimental design. The outcomes of this work could provide direction to researchers regarding sensor evaluation experiments and guide practitioners to effectively select and deploy low-cost sensors for air quality monitoring.

Background: There is growing interest among low- and middle-income countries to introduce electronic immunization registries (EIRs) that capture individual-level vaccine data. While practical EIR guidance documents are available, the real-world implementation experience varies by country. There is an opportunity to learn from countries that have experience implementing EIRs to inform other countries considering the same. Methods: This qualitative study provides a cross-case comparison of the design, development, and deployment of EIRs in three countries: Vietnam, Tanzania, and Zambia. The countries were selected based on PATH’s role in providing technical support to the governments to introduce and scale their EIRs. Through desk review and thematic analyses, we consider findings related to four implementation factors: time, partnerships, financial costs, and technology and infrastructure.Results: The country cases highlight the multi-year timeline required to implement an EIR at scale. Of the 3 countries, only Vietnam has achieved nationwide scale of the EIR after implementing a series of iterative cycles to pilot and redesign the system over 7 years. In terms of partnerships, all three case countries established interdisciplinary national teams with experience in leadership, technology, and immunization, and incorporated end user perspectives from subnational levels in the EIR design and development. It was important for the national government to play an active role to ensure country ownership and sustainability. Financial investment was necessary for design and development, as well as to maintain the EIR beyond the initial deployment, including all recurring costs for system maintenance, updates, and end user support. Finally, technology and infrastructure were important considerations in the EIR design and choice of equipment in each country, and all 3 countries have a local partner to provide ongoing technical support.Conclusions: Comparing implementation factors across these cases highlights practical experience and recommendations that complement existing EIR guidance documents. The findings and recommendations from this study can inform other countries considering or in the process of implementing an EIR.