Search

While urban heat islands (UHIs) have been thoroughly studied in cities worldwide, the specific risks and vulnerabilities related to urban heat in Ethiopia and Africa as a whole has given less attention. Urbanization often replaces green spaces with impervious surfaces, which diminishes natural cooling, precipitation, and water infiltration. This change can significantly affect land surface temperatures (LST) and contribute to UHI formation and its impacts. This study aimed to identify and assess the risk factors linked to UHIs, focusing on pinpointing the most vulnerable areas within cities using principal components explanatory factor analysis (HV-PC-EFA) and the urban heat vulnerability index (UHVI) model. The analysis utilized 19 composite indicators under well-established categories such as exposure, sensitivity, and adaptive capacity to assess potential risk zones. The results from the two models were compared, and their variations were examined. In the HV-PC-EFA model, indicators like urban density and roof type, along with adaptive factors such as vegetation cover, urban thermal field variances, and relative humidity, were not distinctly separated as individual components. This may cause discrepancies in the final outcomes, impacting the spatial distribution and extent of vulnerability. Despite observing some spatial variations in identifying risk areas, the study provides a broad perspective essential for developing evidence-based policies and strategies to enhance cities’ resilience to high temperatures and promote sustainable environments. Given the challenges in modifying existing infrastructure, it is practical to regularly implement adaptive measures, such as preserving and restoring urban water bodies, planting trees, creating green public spaces, and raising public awareness about these risks.

Probabilistic seismic performance assessment method for buildings offers a valuable approach to simulate the broader regional impacts: economic losses, downtime, and casualties. A crucial aspect of this process entails accounting for the spatial correlation of building performances, aiming for an accurate estimation of the probability of extreme regional losses, such as the simultaneous collapse of buildings with similar structural characteristics. In this study, a correlation model based on a Gaussian random field is employed, and several key challenges associated with its application are addressed. In addition, efficiency of five different methods of selecting station records from the same earthquake scenario is compared. The minimum number of earthquake records necessary to achieve a stable correlation result is determined. Additionally, spatial correlations derived from different history earthquake events are compared. By addressing these critical issues, this research contributes to refining the reliability of probabilistic methods for regional resilience assessment.

While the use of innovative seismic control strategies has become widespread in industrial plants, assessment of benefits derived from these measures in a quantifiable form can significantly contribute to a more rational risk-informed decision-making of essential infrastructures. In this paper, a benefit–cost analysis is used to examine three retrofit design schemes of an actual thermal power plant building equipped with different seismic control systems, i.e. buckling-restrained brace (BRB), the hybrid shape memory alloy-buckling restrained brace (SMA-BRB), and the partial mass isolation of heavy industrial equipment. The original design scheme having concentrically braced frames as lateral resisting systems is considered as a benchmark model for comparison purposes. For each mitigation alternative, the benefits against seismic effects were quantified in terms of repair cost and recovery time. The results showed that the retrofit system using SMA-BRB leads to the best performance achieving average reduction in residual drift, peak story drift, resilience index, repair time, and average annual loss by 71%, 27%, 48%, 83%, and 89% compared to the original system, respectively. The proposed benefit–cost analysis framework for industrial power plant buildings can be considered as a practical approach of supporting decision-making for non-technical stakeholders and motivating practicing engineers.

The leaves of 'khat' (Catha edulis Forsk), a plant widely grown in Ethiopia are chewed by local people for their stimulant action. Its branches and part of the leaves are thrown as solid waste. The objectives of this study was to characterize fuel briquette made from this waste disposed in Hawassa city, Ethiopia. First, charcoal fines were prepared from the khat branches and the leaves. The charcoal fines were then mixed with 20% of clay as a binder. It was followed by molding the mixture in a press machine to produce Branch Charcoal Briquette (BCB) and Leaves Charcoal Briquette (LCB). The experimental results showed the Carbonized Branch Briquette Charcoal (CBBC) has an average calorific values of 19,890 kJ/kg and its other performance parameters are also within the acceptable range. Hence, the city's khat residue can be easily processed into CBBC to substitute yearly 480 ton of charcoal and reduce roughly 1,020 tons of carbon dioxide emission to the environment. The Carbonized Leaves Briquette Charcoal (CLBC) did not gave satisfactory results. Hence, it had been recommended to be studied further.

Municipal water managers rely on pipe deterioration models to plan maintenance, repair, and replacement. Although efforts have been made to increase their accuracy, these models are subject to uncertainties in the predictions. In this paper, an optimization procedure of the Bayesian updating period of the parameters of an existing deterioration model is proposed to sequentially reduce the uncertainty in the prediction of the water pipe breakage rate variable. This latter is modeled using a structured geoadditive regression technique where covariates are allowed to have linear (e.g., categorical) and nonlinear (e.g., continuous) relationships with the response variable. Unknown and unobserved covariates are included in the model through a geospatial component that captures spatial auto-correlations and local heterogeneities. The optimization procedure searches through the time series data to identify the optimal updating period horizon that corresponds to the minimum error between the predicted coefficient of determination between predictions and observations using the unupdated and updated models. The process is repeated until the entire time series data is covered. The application of this approach to failure data of large Canadian urban water systems shows a significant reduction in the uncertainty of the parameters and increases the accuracy in the prediction of the output response variable. HIGHLIGHTS Optimization.; Bayesian Updating.; Gaussian Markov Random Fields.; P-Splines.; Deterioration model.;

Abstract The 2020 National Building Code of Canada (NBCC) seismic hazard model (SHM) marks a comprehensive update over its predecessor (NBCC 2015). For different regions in Canada, this will have an impact on the design of new buildings and performance assessment of existing ones. In the present study, a recently developed hybrid building system with reinforced concrete (RC) moment-resisting frames and cross-laminated timber (CLT) infills is assessed for its seismic performance against the latest SHM. The six-story RC-CLT hybrid system, designed using the direct displacement-based method, is located in Vancouver, Canada. Along with very high seismicity, southwestern British Columbia is characterized by complex seismotectonics, consisting of subduction, shallow crustal, and in-slab faulting mechanisms. A hazard-consistent set of 40 ground motion pairs is selected from the PEER and KiK-net databases, and used to estimate the building’s seismic performance. The effects of using steel slit dampers (associated with large hysteresis loops) and flag-shaped energy dissipators (associated with the recentering capability) are investigated. The results indicate that the hybrid system has good seismic performance with a probability of collapse of 2–3% at the 2475-year return period shaking intensity. The hybrid building with steel slit dampers exhibits a collapse margin ratio of 2.8, which increases to 3.5–3.6 when flag-shaped dissipators are used. The flag-shaped dissipators are found to significantly reduce the residual drift of the hybrid building. Additionally, the seismic performance of the hybrid building equipped with flag-shaped dissipators is found to improve marginally when the recentering ratio is increased.

Buried continuous pipelines are prone to failure due to permanent ground deformation as a result of fault rupture. Since the failure mode is dependent on a number of factors, a probabilistic approach is necessary to correctly compute the seismic risk. In this study, a novel method to estimate regional seismic risk to buried continuous pipelines is presented. The seismic risk assessment method is thereafter illustrated for buried gas pipelines in the City of Victoria, British Columbia. The illustrated example considers seismic hazard from the Leech River Valley Fault Zone (LRVFZ). The risk assessment approach considers uncertainties of earthquake rupture, soil properties at the site concerned, geometric properties of pipes and operating conditions. Major improvements in this method over existing comparable studies include the use of stochastic earthquake source modeling and analytical Okada solutions to generate regional ground deformation, probabilistically. Previous studies used regression equations to define probabilistic ground deformations along a fault. Secondly, in the current study, experimentally evaluated 3D shell and continuum pipe–soil finite element models were used to compute pipeline responses. Earlier investigations used simple soil spring–beam element pipe models to evaluate the pipeline response. Finally, the current approach uses the multi-fidelity Gaussian process surrogate model to ensure efficiency and limit required computational resources. The developed multi-fidelity Gaussian process surrogate model was successfully cross-validated with high coefficients of determination of 0.92 and 0.96. A fragility curve was generated based on failure criteria from ALA strain limits. The seismic risks of pipeline failure due to compressive buckling and tensile rupture at the given site considered were computed to be 1.5 percent and 0.6 percent in 50 years, respectively.

Machine learning (ML) based algorithms, due to their ability to model nonlinear and complex relationship, have been used in predicting corrosion pit depth in oil and gas pipelines. Class imbalance and data scarcity are the challenging problems while training ML models. This paper utilized a conditional generative adversarial network (cGAN) to handle class imbalance problem in a corrosion dataset by generating new samples. Utility of the cGAN data augmentation is evaluated by training an artificial neural network (ANN) model. In addition, random oversampling and Borderline-SMOTE data generating techniques are used for comparison with cGAN. The testing accuracy of the ANN model increased greatly when trained by the cGAN based augmented dataset and this model performance improvement can be useful for a pipeline integrity management.

The bio-geophysical effects of land cover classes are considered to be an important factor in land surface temperature variations between urban and suburban areas. This means that major cities are significantly warmer than surrounding suburban or rural areas, which is known as the Urban Heat Island (UHI) effect. The aim of this study was to assess and analyze the spatiotemporal variation, correlation and impact of UHIs on Mekelle city (1990–2020) using remote sensing techniques. The study's primary objective was accomplished using the a number of techniques, including the extraction of LULC classes, estimation of the seasonal LSTs, assessment of UHI and UTFVI, and showing the relationship between LULC and LST as well as the interactions between UHI, UTFVI, and urban LULC classes. By analyzing TIRs/OLI thermal band data after calibrating uncertainty in the images and validating it using the concept of theoretical relationships and least squares fitting method, estimates of local LST, UHI (both in Mekelle and periphery rural areas), and UTFVI were obtained. The result of standard multiple regression models showed that impervious urban land surface, built-up areas, and dry bare soil highly contribute and influence variation at LST intensity caused for the formation of UHI in the study area. The result showed that the maximum UHI value in Mekelle city was 2.73 °C during the dry season in 1990. It decreased slightly to 2.53 °C in 2000 and then increased regularly to 2.83 °C and 2.98 °C in 2010 and 2020, respectively. To determine the city's UHI status in comparison to eight selected peripheral suburban areas, a trend analysis has been done. The UHI intensity of Mekelle city was higher relatively to that of most of the periphery suburban districts, particularly in 2020 (both dry and rainy seasons); this could be due to the city's explosive growth. It's worth noting that the research area affected by the urban heat island effect has grown over time, and as a result, the study area also has severe microclimate conditions that primarily damage the quality of urban life and create the worst conditions for thermal discomfort. The results of this study provide major conceptual understandings of how improper distribution and use of urban land affects the urban environment and fuels the creation of the UHI and thermal discomfort phenomena. Hence policy makers and urban planners should consider the effects of LST and UHI and integrate UHI comprehensive mitigation strategies with urban development patterns, and current and projected local climate changes in order to create sustainable urban environments, cities, and communities. In conclusion, compared to the conventional method, satellite remote sensing provides a faster and more efficient method for researching LST and UHI.

As part of sustainable urban planning, the demand for water and energy (WE) should also be addressed. The Waikato Environment for Knowledge Analysis (WEKA) modeling tool was employed to relate the historical WE consumptions with the population and economic growth scenarios using a linear regression model. The performance of the model was evaluated to properly identify the most influential drivers in each sector. The WE demand prediction was made for each year from 2016 up to 2050. Consequently, the long-term time interval for demand analysis is important rather than the consequent year for planning. The total electric energy demand including residential, street-lighting, commercial and industrial sectors was estimated to be around 14,000 and 53,000 Giga Watt hour (GWh) for the years 2030 and 2050, respectively. These years' forecasted petroleum demand was around 8840 and 30,140 for diesel, 13,860 and 52,700 for gasoline, and 1230 and 9890 GWh for kerosene and the water demand including residential, commercial and industrial sectors were 520 and 1600 million cubic meters (MCM). The proposed methodology can comfortably be used to predict the urban WE demand corresponding to economic (gross domestic product and per capita income) and population growth at different scenarios which could support policy makers. HIGHLIGHTS Predicting long-term water-energy demand is important for planning.; A linear regression model is used for long-term water-energy demand predicting.; The water-energy demand in urban areas is increasing.; Population and economic growth are the main factors which are highly affecting the urban water-energy demand.; Identifying the most influential drivers on water-energy demand is important for water-energy supply planning.; Technological factors (such as water loss, energy loss) are commonly considered in demand prediction.;

External corrosion is one of the leading causes of pipe failure in the oil and gas industry. In this study, a Bayesian belief network (BBN) model has been developed using corrosion rate (CR) data obtained from experimental test results and analytical burst failure models. The BBN model for CR was coupled with a time marching simulation to obtain corrosion defects and quantify failure pressure capacity. Finally, in a reliability framework, the failure pressure capacity was coupled with operating pressure to obtain the probability of failure. Furthermore, the developed BBN model was used to perform a parametric study to identify the critical parameters for the CR. The outcome of the study indicated that the proposed BBN model can be useful to integrate experimental and analytical models to derive reliability of a pipeline operating under various conditions.

The Ethiopia energy mix is dominated by hydro-generation, which is largely reliant on water resources and their availability. This article aims to examine the impacts of severe drought on electric power generation by developing a Drought Scenario. OSeMOSYS (an open source energy modelling tool) was used to perform the analyses. The results were then compared with an existing reference scenario called “New Policy Scenario”. The study looked at how power generation and CO2 emissions would be altered in the future if reservoir capacity was halved due to drought. Taking this into account, the renewable energy share decreased from its 90% in 2050 to 81% in 2065, which had been 98% to 89% in the case of New Policy Scenario. In another case, CO2 emissions also increased from 0.42 Mt CO2 in 2015 to 7.3 Mt CO2 in 2065, a 3.3 Mt CO2 increase as compared to the New Scenario. The results showed how a prolonged period of drought would reduce the river flows and lead to an energy transition that may necessitate the installation of other concurrent alternative power plants. The study suggested ways to approach energy mix, particularly for countries with hydro-dominated power generation and those experiencing drought.

Seismically deficient bridges, coupled with their aging and deterioration, pose significant threat to safety, integrity, and functionality of highway networks. Given limited funds available for bridge retrofitting, there is a need for an effective management strategy that will enable decision-makers to identify and prioritize the high-risk bridges for detailed seismic evaluation and retrofit. In this paper, a risk-based preliminary seismic screening technique is proposed to rank or prioritize seismically-deficient bridges. The proposed risk assessment entails hierarchically integrating seismic hazard, bridge vulnerability, and consequences of failure. The bridge vulnerability accounts for chloride-induced corrosion deterioration mechanisms. A Bayesian belief network based modeling technique is used to aggregate through the hierarchy and generate risk indices. The efficacy of the proposed method is illustrated on two existing bridges that are assumed to be located in high seismic zones and designed under different standards concerning their structural safety under seismic loads and durability performance.

This paper presents collapse risk assessment of code-conforming reinforced concrete moment resisting frame (RC MRF) buildings located in Vancouver, Canada. This assessment investigates the three- and six-story of regular RC MRF building systems, with and without unreinforced masonry infill wall, representing low- to mid-rise structures. These buildings are designed according to the current National Building Code of Canada and detailed based on the 2014 Canadian Standards Association A23.3 standard provision for high seismic regions. Two different ductility classes of seismic building design, namely ductile and moderately ductile, are considered to identify the capability, equality, and/or difference of the seismic performance of these designed buildings. Nonlinear dynamic analysis is applied in the performance-based seismic assessment procedures to assess the collapse response of structural for the set of 50 pair ground motion records. Next, the seismic fragility curves are developed through incremental dynamic analysis. Finally, mean annual frequency of collapse is calculated through combination of fragility curve and hazard curve. The results indicate that the bare RC buildings are vulnerable to earthquake-induced collapse when the number of the story increased. The presence of the URM infill walls significantly influence the collapse behavior of the frame structure. Compared to moderately ductile MRF buildings, ductile MRF buildings show a better collapse performance, are strongly influenced by the capacity of the building system.

This article presents a seismic performance evaluation framework for reinforced concrete (RC) buildings, comprising shear walls and gravity frames. The evaluation is undertaken within a performance-based earthquake engineering framework by considering regional seismicity and site-specific ground motion selection. Different engineering demand parameters (EDPs), i.e., maximum interstory drift ratio (MaxISDR) and energy-based damage index, are considered as performance indicators. Various prediction models of EDPs are developed by considering four ground motion intensity measures (IMs), i.e., spectral acceleration at the fundamental period, Arias intensity, cumulative absolute velocity (CAV), and significant duration of ground motion. For this study, a 15-story RC building, located in Vancouver, BC, Canada, is considered as a case study. By using 50 mainshock and 50 mainshock–aftershock (MS-AS) earthquake records (2 horizontal components per record and bidirectional loading), non-linear dynamic analyses are performed. Subsequently, the calculated MaxISDRs and damage indices are correlated with suitable IMs using cloud analysis, and the most efficient IM-EDP prediction models are selected by comparing standard deviations (SDs) of the regression errors. The MaxISDR of the shear walls is less than 1% for the mainshock and MS-AS records. The energy-based damage index shows sensitivity to delineate impact of earthquake types and aftershocks. The CAV is showed to be the most efficient IM for the energy-based damage index.

The leaves of 'khat' (Catha edulis Forsk), a plant widely grown in Ethiopia are chewed by local people for their stimulant action. Its branches and part of the leaves are thrown as solid waste. The objectives of this study was to characterize fuel briquette made from this waste disposed in Hawassa city, Ethiopia. First, charcoal fines were prepared from the khat branches and the leaves. The charcoal fines were then mixed with 20% of clay as a binder. It was followed by molding the mixture in a press machine to produce Branch Charcoal Briquette (BCB) and Leaves Charcoal Briquette (LCB). The experimental results showed the Carbonized Branch Briquette Charcoal (CBBC) has an average calorific values of 19,890 kJ/kg and its other performance parameters are also within the acceptable range. Hence, the city's khat residue can be easily processed into CBBC to substitute yearly 480 ton of charcoal and reduce roughly 1,020 tons of carbon dioxide emission to the environment. The Carbonized Leaves Briquette Charcoal (CLBC) did not gave satisfactory results. Hence, it had been recommended to be studied further. [ABSTRACT FROM AUTHOR]