Future Condition Research Articles

Development of framework for performance prediction of flexible road pavement in Nigeria using Fuzzy logic theory

ABSTRACT Though several attempts have been made in the past to develop performance prediction models for flexible road pavement in Nigeria, insufficient dataset remained the major problem of using such models. This study used Fuzzy logic theory which is capable of handling the challenge of imprecise dataset to develop a framework for performance prediction of flexible road pavement in Nigeria. A Fuzzy Inference System was developed using MATLAB software. The attributes used included; Initial Pavement Surface Condition, Age of pavement, Resilient Modulus of subgrade soil, Average Truck load per day, Average Annual Air Temperature and Rainfall to predict the Future Pavement Surface Condition (FPSC). The model was calibrated using observed logical behaviour of pavement to fit engineering experience and judgement. A goodness-of-fit test showed high level of accuracy at 81.63% which was validated at 74.13% using extrapolated dataset of the same source. The study proposed 5120 mutually exclusive decision rules for performance prediction of flexible road pavement based on permutation theory. Though there was no current and well-spread dataset that described the present pavement condition to calibrate the decision rules, a framework for performance prediction of flexible road pavement using Fuzzy logic theory was developed for pavement engineers in Nigeria.

Modelling future changes to the hydrological and thermal regime of unaltered streams using projected changes in climate to support planning for sensitive species management

AbstractClimate change will alter stream habitats through precipitation and air temperature changes and potentially threaten species that rely on contemporary streamflow and stream temperature regimes. Habitat projections are therefore critical to inform management decisions. Past and ongoing research has improved streamflow and temperature modelling in ungauged regions, but no studies merge these advancements with climate modelling for regional streamflow and stream temperatures predictions that describe stream habitat change. Here, we predict change in streamflow and stream temperature at the reach scale using projections from downscaled global climate models (GCMs) and the ‘business as usual’ carbon emission scenario. We focus on unaltered streams in six southern California watersheds using data from baseline (1982–2014) and projected end‐of‐century (2082–2100). Stream temperature is projected to increase regionally, with high‐elevation stream reaches increasing most rapidly. There is less consistency in the streamflow projections, but a spatial and temporal homogenization of stream flow characteristics was predicted, that is, flows become more similar across the region with less inter‐annual variation. Additionally, there is a regional trend towards larger high flow magnitudes and more storm events. Despite the increased frequency and magnitude of storm events, high‐elevation streams are predicted to become drier for a greater portion of the year. Conversely, low‐elevation streams are predicted to have longer hydroperiods. Mapping future streamflow and stream temperatures at the reach scale can direct conservation efforts to streams that remain suitable, restoration to areas that decrease in suitability for target species, and support water policies that consider future stream condition.

Projecting expansion range of Selaginella zollingeriana in the Indonesian archipelago under future climate condition

Abstract. Setyawan AD, Supriatna J, Nisyawati, Nursamsi I, Sutarno, Sugiyarto, Sunarto, Pradan P, Budiharta S, Pitoyo A, Suhardono S, Setyono P, Indrawan M. 2021. Projecting expansion range of Selaginella zollingeriana in the Indonesian archipelago under future climate conditions. Biodiversitas 22: 2088-2103. The expansion of plant species to outside areas of its original localities has attracted great interest in theoretical ecology. The scientific curiosity of such phenomenon is even deeper when the geographical expansion is confined by natural boundaries and affected by environmental changes, including climates. This study aimed to predict the current suitable habitat niche of Selaginella zollingeriana Spring, a species with original distribution in Java Island, and to project its potential suitable niche in the Indonesian archipelago accounting for future climate conditions. In doing so, we applied the Ecological Niche Modelings (ENMs) using MaxEnt algorithm by employing 30 presence data of S. zollingeriana and twelve enviro-climatic variables. The model predicted around 17.22% (22,095 km2) of the Java Island area is potentially suitable for current habitat niche of S. zollingeriana, consisting of 10.93% (14,028 km2), 4.75% (6,097 km2), and 1.54% (1,970 km2) of low, medium, and high suitability areas, respectively. Under future scenarios, the model predicted the possibility of species expansion into the other four big islands (i.e., Sumatera, Borneo, Sulawesi, and Papua). Nonetheless, the model also predicted a declining trend of the availability of suitable niches wherein from 2030 to 2080 the modeled niche declined about 58% and 59% under the most optimistic and most pessimistic climate change projections, respectively. While this study provides a primary example in predicting species expansion in tropical archipelago, similar studies in a range of contexts (e.g., species, region) are recommended to add more evidence to strengthen the theoretical ground of expansion ecology under climate changes.

Resource Trading in Edge Computing-Enabled IoV: An Efficient Futures-Based Approach

Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (EC-IoV), where a forward contract is used to facilitate resource trading-related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties of the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings mutually beneficial utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.

Mathematical fitting model and analysis in describing Covid-19 viruses outbreak in Indonesia

The outbreak of novel coronavirus at the end of 2019 (covid-19) has become a global concern due to its fast transmission from human-to-human. It is first time discovered in Wuhan, Hubei Province in China. In this research, the author tries to construct epidemiological model that consist of six subpopulations such as susceptible (S), exposed (E), infected (I), isolated (Q), recovered (R), and dead (D) population. Those compartments will be analyzed to get basic reproduction number (R0). The data used in this research is secondary data that obtained from the recent study and various news report related to the covid-19 outbreak. Fitting method will be carried out to describe the virus outbreak and see the trend of the future condition based on the real observation data and the mathematical function that has been determined. The expected result of this research is that the mathematical fitting model and its analysis helping the epidemiologist and health practitioner to determine the right treatment to stop the outbreak so that the society could be back to virgin population.

Promoting public transport as a strategy to reduce GHG emissions from private vehicles in Kuwait

Traffic congestion is one of the most important problems that faces metropolises despite of the measures being taken to mitigate and reduce such problem. Traffic congestion in Kuwait requires a rapid solution, especially in Kuwait City. Public transportation can decrease the number of private vehicles to ease the traffic congestion, provide a good impact on the environment, and enhance the cost-effectiveness, reliability, and accessibility of the road network. This research encourages the use of public transportation for congestion reduction. All needed data were collected from different sources. They were used to simulate the future network condition in terms of reducing traffic congestion, fuel consumption, and gas emissions. The obtained data were processed using synchro software. The findings showed a reduction in the congestion by about 45% in some streets. The environmental impact was enhanced with a noticeable level of reduction in the gas emissions. The fuel consumption was reduced to 46% after the improved data. The consumer surplus was 7848.32 KD while the social welfare was 57258.6 KD for this study.

Water quality characteristics and modeling of Tasik Aman, USM, Penang

Tasik Aman was constructed in 1990 as a flood retention pond in the Universiti Sains Malaysia, Main Campus, Penang, Malaysia. Tasik Aman has been experiencing accumulation of excess nutrients from the surrounding area mainly from Sungai Gambir, causing high algae population in the water body. Thus, a study was carried out in December 2019, focusing on the current water quality determination of Tasik Aman, to predict the future water quality condition of the lake. Four sites were designated to conduct water quality analysis comprising of water temperature, dissolved oxygen (DO), pH, transparency, biological oxygen demand (BOD), total dissolved solids, total suspended solids, conductivity, orthophosphate, chlorophyll-a, total phosphorus, ammonia, nitrite, and nitrate. Some physico-chemical parameters were measured in-situ while some were measured in the lab by analyzing the water samples collected in the field in accordance with APHA (2005). DO and BOD data were modeled using Water Quality Analysis Simulation Program (WASP). Tasik Aman showed high readings in temperature, TSS, TDS, conductivity, DO, BOD, chlorophyll-a, and nitrate-nitrogen. One-way ANOVA further revealed significant differences of DO, TDS, conductivity, transparency, orthophosphate, nitrate, and chlorophyll-a among the stations of Tasik Aman (p<0.05). Based on WASP analysis, simulated graphs produced for Tasik Aman showed a decrease in DO and an increase in BOD level proving that the lake contain high nutrient content that may exacerbate the eutrophication.

Multi Criteria Analysis of Road Handling Type at Banda Aceh – Meulaboh National Road Segment Indonesia

The western and southern path of highway that connects Banda Aceh to Meulaboh City is national road. The distance between the two cities is 246 kilometers. On the connecting link between the two cities there are at least three locations in the mountainous area. Roads geometric in these locations have low capacity, uphill and downhill with high grade and also have a small curved radius. These conditions cause low levels of road services and the risk of traffic accidents is high. These three segment area are: Paro, Kulu and Geurutee mountain area. Traffic at these three road locations is often hampered and also has a high risk in terms of safety. Consider with current and future traffic condition, there is a discourse to increase the capacity of existing roads or plans for new road trace on the mountain with tunnels construction. In the feasibility study stage, there will generally be alternatives that must be selected for the best alternative. To choose the best alternative, it should be done by considering the many criteria. For this reason, this paper is the selection of the best route using the Multi Criteria Analysis (MCA) method.

Data‐based Approach for Fault Diagnosis of Hydropower Rotors

AbstractThe poster presents a novel approach to diagnose rotordynamic faults like unbalance and coupling misalignment from measured vibration. For that purpose, a large database of virtual hydropower rotors and their vibration has been calculated. The goal is to create a data‐driven diagnosis system from this database, that will be applicable to a variety of real hydropower rotors. In a first step, a gradient boosting regression algorithm has been applied to the database with some preprocessing and showed promising results. A future condition monitoring system based on this idea will be easily applicable to a variety of hydropower rotors without the need of machine‐specific models. It will help machine operators to monitor faults in order to start targeted countermeasures when they are needed, avoiding unscheduled downtimes and increasing overall cost‐efficiency of their machine.

Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty

Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.

A hybrid predictive methodology for head checks in railway infrastructure

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

A combination of climate, tree diversity and local human disturbance determine the stability of dry Afromontane forests

BackgroundAnthropogenic disturbances are increasingly affecting the vitality of tropical dry forests. The future condition of this important biome will depend on its capability to resist and recover from these disturbances. So far, the temporal stability of dryland forests is rarely studied, even though identifying the important factors associated with the stability of the dryland forests could serve as a basis for forest management and restoration.MethodologyIn a degraded dry Afromontane forest in northern Ethiopia, we explored remote sensing derived indicators of forest stability, using MODIS satellite derived NDVI time series from 2001 to 2018. Resilience and resistance were measured using the anomalies (remainders) after time series decomposition into seasonality, trend and remainder components. Growth stability was calculated using the integral of the undecomposed NDVI data. These NDVI derived stability indicators were then related to environmental factors of climate, topography, soil, tree species diversity, and local human disturbance, obtained from a systematic grid of field inventory plots, using boosted regression trees in R.ResultsResilience and resistance were adequately predicted by these factors with an R2 of 0.67 and 0.48, respectively, but the model for growth stability was weaker. Precipitation of the wettest month, distance from settlements and slope were the most important factors associated with resilience, explaining 51% of the effect. Altitude, temperature seasonality and humus accumulation were the significant factors associated with the resistance of the forest, explaining 61% of the overall effect. A positive effect of tree diversity on resilience was also important, except that the impact of species evenness declined above a threshold value of 0.70, indicating that perfect evenness reduced the resilience of the forest. Precipitation of the wettest month was the most important factor explaining 43.52% of the growth stability variation.ConclusionA combination of climate, topographic factors and local human disturbance controlled the stability of the dry forest. Also tree diversity is an important stability component that should be considered in the management and restoration programs of such degraded forests. If local disturbances are alleviated the recovery time of dryland forests could be shortened, which is vital to maintain the ecosystem services these forests provide to local communities and global climate change.

Climate change impact on economic and irrigation requirements for sugarcane crop in Egypt

Availability of irrigation water is considered one of the major challenges faced by Egypt during current time and will be more difficult in the future with the limited water resources and linearly increase of population. The current study investigates irrigation demand for sugarcane cultivation areas in the Middle and Upper Egypt Governorates (Menia, Asyut, Sohag, Qena, Luxor and Aswan) during current time and under RCPs scenarios. The current data was collected from 1971 till 2000. The RCP data were collected for different scenarios (RCP 3, RCP 4.5, RCP 6 and RCP 8.5) during three time series (2011-2040, 2041-2070 and 2071-2100). The highest evapotranspiration values during current and future conditions was found in Luxor and Aswan governorates; while the lowest evapotranspiration values were recorded in Menia and Asyut. All RCPs scenarios were significantly higher than current conditions. Moreover, the highest irrigation requirements under RCP scenarios were recorded in Aswan and Luxor under RCP 8.5 during 2071-2100 time series. While the lowest irrigation requirement under climate change were found in Menia under RCP3 during 2011-2040 time series. Irrigation requirement for sugarcane under current and future condition was demonstrated by interpolated maps.

Impact of Increased Nutrients and Lowered pH on Photosynthesis and Growth of Three Marine Phytoplankton Communities From the Coastal South West Atlantic (Patagonia, Argentina)

Effect of global change variables on the structure and photosynthesis of phytoplankton communities was evaluated in three different sites of the Patagonian coast of Argentina: enclosed bay (Puerto Madryn, PM), estuarine (Playa Unión, PU), and open waters (Isla Escondida, IE). We exposed samples to two contrasting scenarios: Present (nutrients at in situ levels) vs. Future (with lowered pH and higher nutrients inputs), and determined growth and photosynthetic responses after 2 days of acclimation. Under the Future condition phytoplankton growth was higher in the estuarine site compared to those in PM and IE. This effect was the most pronounced on large diatoms. While the increase of photosynthetic activity was not always observed in the Future scenario, the lower photosynthetic electron requirement for carbon fixation (Φe,C = ETR/PmB) in this scenario compared to the Present, suggests a more effective energy utilization. Long-term experiments were also conducted to assess the responses along a 4 days acclimation period in PU. Diatoms benefited from the Future conditions and had significantly higher growth rates than in the Present. In addition, Φe,C was lower after the acclimation period in the Future scenario, compared to the Present. Our results suggest that the availability, frequency and amount of nutrients play a key role when evaluating the effects of global change on natural phytoplankton communities. The observed changes in diatom growth under the Future scenario in PU and IE and photosynthesis may have implications in the local trophodynamics by bottom up control.

Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

Monitoring-based evaluation of dynamic characteristics of a long span suspension bridge under typhoons

The Xihoumen Bridge, with a main span of 1650 m and two side spans of 578 m and 485 m, respectively, is a long-span suspension bridge spanning the Jintang and Cezi islands in Zhejiang Province, China. In order to establish a reference baseline for vibration mitigation and condition assessment of the bridge under typhoons, the wind field properties, vibration energy distribution, and modal parameters of the bridge are identified through in-situ monitoring. Dynamic responses at different locations on main span and wind speeds at 1/2 main span of the bridge under 10 typhoons and under normal wind were collected for this study. Vibration energy distribution is estimated using wavelet packet transform. Modal parameters are identified by the peak-picking (PP) and data-driven stochastic subspace identification (SSI-DATA) methods. The results indicate that in the low-frequency range, the fluctuating wind energy of typhoons changes more significantly than that of normal wind. More than 80% of the vibration energy is concentrated in the frequency band (0–1.5625 Hz) under typhoons, while nearly 90% of the vibration energy under normal winds is evenly distributed in three frequency bands (0–1.5625 Hz, 1.5625–3.125 Hz, and 3.125–4.6875 Hz). When using typhoon-induced dynamic responses as input, the SSI-DATA method performs better than the PP method in the identification of modal parameters. The results obtained in this study can be used as a baseline for future structural condition assessment of the bridge after typhoon events.

Loops in dS/CFT

We consider the semi-classical expansion of the Bunch-Davies wavefunction with future boundary condition in position space for a real scalar field, conformally coupled to a classical de Sitter background in the expanding Poincaré patch with quartic selfinteraction. In the future boundary limit the wave function takes the form of the generating functional of a Euclidean conformal field theory for which we calculate the anomalous dimensions of the double trace deformations at one loop order using results obtained from Euclidean Anti de Sitter space. We find analytic expressions for some subleading twist operators and an algorithm to obtain expressions for general twist.

Predictive information and maintenance optimization based on decision theory: a case study considering a welded joint in an offshore wind turbine support structure

Predictive information and maintenance optimization for deteriorating structures is concerned with scheduling (a) the collection of information by inspection and monitoring and (b) maintenance actions such as repair, replacement, and retrofitting based on updated predictions of the future condition of the structural system. In this article, we consider the problem of jointly identifying—at the beginning of the service life—the optimal inspection time and repair strategy for a generic welded joint in a generic offshore wind turbine structure subject to fatigue. The optimization is performed based on different types of decision analyses including value of information analyses to quantify the expected service life cost encompassing inspection, repair, and fatigue damage for all relevant combinations of inspection time, repair method, and repair time. Based on the analysis of the expected service life cost, the optimal inspection time, repair method, and repair time are identified. Possible repair methods for a welded joint in an offshore environment include welding and grinding, for which detailed models are formulated and utilized to update the joint’s fatigue performance. The decision analyses reveal that an inspection should be scheduled approximately at mid-service life of the welded joint. A repair should be performed in the same year after an indication and measurement of a fatigue crack given an optimal inspection scheduling. This article concludes with a discussion on the results obtained from the decision and value of information analyses.

An experimental determination of the relationship between the minimum height of landslide dams and the run-out distance of landslides

Landslides frequently occur on mountain slopes due to earthquakes and rainfall. When a landslide occurs near a river, the landslide mass moves at a certain speed towards the river channel and eventually stops at the channel either due to frictional resistance or by being blocked by the upslope on the opposite bank of the river. If the width of the river channel at the landslide site (BW) is narrow and the volume of the landslide (VL) is sufficient, the landslide may block the river channel, resulting in landslide dams. Landslide dam is a significant hazard in mountainous area, when dam failure will cause severe destruction and affect downstream area. The frequency, scale, and disaster of landslide dam will be more serious than before based on global climate change. To reduce disasters caused by landslide dams, rapid assessments of dam stability and dam-break discharge must be conducted in the early stages of the formation of landslide dams. One of the essential parameters for these assessments is the minimum height of the landslide dam ( $$ {H}_{D_{\mathrm{min}}} $$ ). However, landslides may occur in remote, inaccessible mountainous areas where the geometrical parameters of landslide dams may not be immediately obtained, making it difficult to perform emergency risk assessments of barrier lakes. The magnitude of $$ {H}_{D_{\mathrm{min}}} $$ changes due to the effects of the run-out distance of the landslide (Lrun ‐ out) and BW, but a quantitative relationship has not been reported in the literature. Hence, this study investigated the effects of VL, the slope angle (θS), the maximum height of the landslide (HL), BW, and other parameters on $$ {H}_{D_{\mathrm{min}}} $$ through laboratory experiments. The relationship between Lrun ‐ out, BW, and $$ {H}_{D_{\mathrm{min}}} $$ was determined, and a dimensionless method for evaluating $$ {H}_{D_{\mathrm{min}}} $$ was established. This method can be applied to predict the minimum height of landslide dams when the position of the landslide is known. Additionally, when landslide dams form but $$ {H}_{D_{\mathrm{min}}} $$ is not known due to difficulties in on-site study, this method can quickly assess the height of the dam and provide an assessment of the risks related to the landslide dam. This greatly reduces the uncertainties associated with the prevention and emergency management of landslide dams, and can assist government authorities to set up the disaster early warning plans of landslide dam under future climate change condition.

Integrating BIM into sensor-based facilities management operations

Integrating BIM into sensor-based facilities management operations