Search

The reuse of load-bearing building components has the potential to promote the circular economy in the building sector. One recent aspect of the efforts to improve reuse rates in buildings is estimating the reusability of the structural elements. This study develops a probabilistic predictive model using advanced supervised machine learning methods to evaluate the economic reusability of the load-bearing building elements. The results of sensitivity analysis and visualization techniques used in this study reveal that the most important economic factor is the need to purchase reused elements early in a project, which could have cash flow implications. The other most important factors are the potential financial risks, the procurement process, and the labour cost. This study unveils that the relationship between variables is not linear, and none of the identified factors could alone determine if an element is reusable or not. This study concludes that the complex interdependencies of factors affecting reuse cause a high level of uncertainty about the feasibility of reusing the load-bearing building structural components from an economic aspect. Nonetheless, this paper reveals that by using the probability theory foundations and combining it with advanced supervised machine learning methods, it is possible to develop tools that could reliably estimate the economic reusability of these elements based on affecting variables. Therefore, the authors suggest utilizing the approach developed in this research to promote the circularity of materials in different subsectors of the construction industry.

The high velocity, variety and volume of data generation by today's systems have necessitated Big Data (BD) analytic techniques. This has penetrated a wide range of industries; BD as a notion has various types and characteristics, and therefore a variety of analytic techniques would be required. The traditional analysis methods are typically unable to analyse spatial-temporal BD. Interpolation is required to approximate the values between the already existing data points, yet since there exist both location and time dimensions, only a multivariate interpolation would be appropriate. Nevertheless, existing software are unable to perform such complex interpolations. To overcome this challenge, this paper presents a layer by layer interpolation approach for spatial-temporal BD. Developing this layered structure provides the opportunity for working with much smaller linear system of equations. Consequently, this structure increases the accuracy and stability of numerical structure of the considered BD interpolation. To construct this layer by layer interpolation, we have used the good properties of Radial Basis Functions (RBFs). The proposed new approach is applied to numerical examples in spatial-temporal big data and the obtained results confirm the high accuracy and low computational cost. Finally, our approach is applied to explore one of the air pollution indices, i.e. daily P M 2.5 concentration, based on different stations in the contiguous United States, and it is evaluated by leave-one-out cross validation.

The focus of the most recent experimental studies of the welded shoe-base connection has been the fatigue strength in the long-life regime. A fracture mechanics-based life prediction model developed for the as-tested case compares favorably with experimental results. Scanning Electron Microscope (SEM) results were used to better understand the role of striations in fatigue life estimation of as-tested specimens. In addition, a parametric study using the finite element method investigated geometric parameters affecting the stresses local to the shoe-base detail. Local stresses from the parametric study provided a basis for fracture mechanics models of shoe-base details with altered geometries.

Hand-holes are used in the body of welded aluminum light poles to access electrical wiring for installation and maintenance of luminaires. Light poles are slender and subjected to cyclic loading due to the wind. Localized fatigue cracking around hand-holes has been observed in the field, but few studies have focused on the resistance of welded aluminum hand-holes. In this study, four-point bending fatigue tests were conducted on specimens with welded aluminum hand-hole details. Twenty welded aluminum specimens, each with two hand-holes, were tested under fatigue loading. Among the forty details, thirty-two failed as a result of fatigue cracking. Fatigue test results used to develop a design S-N curve revealed a resistance in reasonable agreement with the Category D design S-N curve of the Aluminum Design Manual. Fatigue cracking initially observed in the throat of fillet weld joining the reinforcement ring to the pole subsequently extended into the tube and/or through the reinforcement casting. Finite element analysis was used to augment the experimental studies in an effort to explain the observed cracking behavior.

In this article, we study symmetric designs admitting a flag-transitive and point-primitive automorphism group whose socle is a symplectic group of dimension four. We prove a reduction theorem severely restricting the possible parameters of such designs, and we show that such a parameter set belongs to one of the eight infinite families of parameter sets. For four families of these parameter sets, we know two sporadic examples of symmetric designs with parameters ( 45 , 12 , 3 ) and ( 36 , 15 , 6 ) arose from PSp 4 ( 3 ) and two infinite families of 2-designs namely the complement of a point-hyperplane design of PG ( 3 , q ) and the complement of a Higman design. The existence of any symmetric designs with the remaining four parameter sets is still open.

The construction sector is the largest consumer of raw materials and accounts for 25%–40% of the total CO2 emissions globally. Besides, construction activities produce the highest amount of waste among all other sectors. According to the waste hierarchies, reuse is preferred to recycling; however, most of the recovery of construction and demolition wastes happens in the form of recycling and not reuse. Part of the recent efforts to promote the reuse rates includes estimating the reusability of the load-bearing building components to assist the stakeholders in making sound judgements of the reuse potentials at the end-of-life of a building and alleviate the uncertainties and perceived risks. This study aims to develop a probabilistic model using advanced supervised machine learning techniques (including random forest, K-Nearest Neighbours algorithm, Gaussian process, and support vector machine) to predict the reuse potential of structural elements at the end-of-life of a building. For this purpose, using an online questionnaire, this paper seeks the experts’ opinions with actual reuse experience in the building sector to assess the identified barriers by the authors in an earlier study. Furthermore, the results of the survey are used to develop an easy-to-understand learner for assessing the technical reusability of the structural elements at the end-of-life of a building. The results indicate that the most significant factors affecting the reuse of building structural components are design-related including, matching the design of the new building with the strength of the recovered element.

Efficient life-cycle management of civil infrastructure systems under continuous deterioration can be improved by studying the sensitivity of optimised preventive maintenance decisions with respect to changes in model parameters. Sensitivity analysis in maintenance optimisation problems is important because if the calculation of the cost of preventive maintenance strategies is not sufficiently robust, the use of the maintenance model can generate optimised maintenances strategies that are not cost-effective. Probabilistic sensitivity analysis methods (particularly variance based ones), only partially respond to this issue and their use is limited to evaluating the extent to which uncertainty in each input contributes to the overall output's variance. These methods do not take account of the decision-making problem in a straightforward manner. To address this issue, we use the concept of the Expected Value of Perfect Information (EVPI) to perform decision-informed sensitivity analysis: to identify the key parameters of the problem and quantify the value of learning about certain aspects of the life-cycle management of civil infrastructure system. This approach allows us to quantify the benefits of the maintenance strategies in terms of expected costs and in the light of accumulated information about the model parameters and aspects of the system, such as the ageing process. We use a Gamma process model to represent the uncertainty associated with asset deterioration, illustrating the use of EVPI to perform sensitivity analysis on the optimisation problem for age-based and condition-based preventive maintenance strategies. The evaluation of EVPI indices is computationally demanding and Markov Chain Monte Carlo techniques would not be helpful. To overcome this computational difficulty, we approximate the EVPI indices using Gaussian process emulators. The implications of the worked numerical examples discussed in the context of analytical efficiency and organisational learning.\ud \ud

This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.

This paper presents three poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) composite-based sensors for detecting volatile organic compunds (VOCs) in the presence of water vapor applicable in breath based sensing. Three sensors with varying sensitivities to acetone, water, ethanol, isoprene, and 2-ethylhexyl acetate (2-EHA), representing VOCs found in breath, have been developed. A sensor fabricated by spray coating PVDF-HFP/C65 composite ( Sensor-1 ) exhibited a 52.6% increase in resistance when exposed to acetone, while the change was less than 0.34% for water. The resistance changes for ethanol, isoprene, and 2-EHA, were 5.6%, 3%, and 0.11%, respectively. The second sensor ( Sensor-2 ), with a two-layer design of PVDF-HFP and PVDF-HFP/C65, was fabricated by a spin coating method, which exhibited improved response times. The response times of Sensor-2 compared to Sensor-1 decreased by 52% for acetone, 92% for water, 30% for ethanol, and 61% for isoprene. In the third sensor ( Sensor-3 ), the addition of carbon nanotube (CNT) in the composite (PVDF-HFP/C65/CNT) caused a decrease in resistance when exposed to water. This is in contrast to Sensors 1 & 2 where resistance increased for all VOCs, and provides a higher selectivity for the system. The results from the three sensors were analyzed using principal component analysis (PCA) and it was demonstrated that the combined responses of these sensors provide a high selectivity. The sensor fabrication, testing methods, and results are presented and discussed.

This study presents the synthesis, fabrication, and development of a selective polyetherimide (PEI)/carbon black (CB) sensor, engineered to detect aldehydes such as nonanal. Using a novel approach, spin coating of the PEI/CB in 1-methyl-2-pyrrolidone (NMP) was followed by a reduction of the composite through heat treating at 200 °C resulting in a highly sensitive and selective film (with low hydrophilicity) for detection of nonanal. This represents a key step, as it decreased surface oxygen and increased surface amine/imine sites. XPS analysis demonstrated the engineered surface was comprised of >3.5% nitrogen (including 1.2% amines/imines) and displayed a contact angle of 80°. The sensor response to nonanal (1 ppm to 80 ppm) ranged from 0.02% to 1.9%. Fabricated sensors showed over 14 times more sensitivity to nonanal than volatiles with similar molecular weights but different functional groups such as 2-nonanone, dodecane, and 1-octanol. This confirms the sensing film has high selectivity to aldehydes over ketones, hydrocarbons, and alcohols. Importantly, the proposed sensor maintained its response to nonanal over a period of 36 days. In addition, a multifactor machine learning algorithm was developed based on principal component analysis (PCA) to detect the type and concentration of a VOC using the temporal response of the sensor. Fabrication, characterization, testing methods, and results are presented and discussed.