Telecommunications and Energy Infrastructure Sharing: Technical and Socio-Economic Impact Analysis in a Multi-Operator Environment in Burundi

The sharing of telecommunications infrastructure and power supply equipment is currently an applicable and very common model for grouping signal transmission and reception equipment and their power supply on the same site to ensure coverage of fixed, mobile, Internet and radio and television broadcasting networks. This study consists of producing an inventory of telecommunications and energy infrastructure sharing, focusing on the one hand on analyzing the impacts of active and passive sharing of telecommunications infrastructure from a technical point of view, particularly in terms of legal framework, deployment, coverage and exposure to electromagnetic radiation, and on the other hand on identifying the effects of infrastructure sharing from a socio-economic point of view in a multi-operator mobile telephony environment, by indicating the economic value of the revenue generated as a result of infrastructure sharing. Finally, the results will contribute to identify strategies for ensuring maximum deployment and coverage of the country, and for developing the information and communication technologies (ICT) sector in order to contribute to the digital transformation by digitising services using mobile telephony and the Internet in Burundi.

Earthquakes and Sustainable Infrastructure- Neodeterministic (NDSHA) Approach Guarantees Prevention Rather Than Cure

The reduction of seismic disaster risk hinges on the reliable assessment of seismic hazard.In the realm of seismic hazard assessment(SHA),there have historically been two methods:the probabilistic SHA(PSHA)and the deterministic SHA(DSHA)(Reiter,1990).Hazard is defined as inherent physical characteristic that poses potential threats to people,property,or environment.Within the context of seismic hazard,the main purpose of hazard analysis is to quantitatively assess ground shaking level at a site,through either DSHA or PSHA method.It is essential to quantitatively evaluate ground motion level at a target site.

Risk Acceptability and Cost-Effectiveness of Protective Measures Against Terrorist Threats to Built Infrastructure Considering Multiple Threat Scenarios

Decisions are often needed about the need and/or extent of protective measures against explosive blast loads on built infrastructure. A decision support analysis considers fatality risks and cost-effectiveness of protective measures expressed in terms of expected cost spent on risk reduction per life saved for terrorist threats to infrastructure. The analysis is applicable to any item of infrastructure, but in this paper is applied to casualties arising from building facade glazing damage. Risks may be compared with risk acceptance criteria in the form of quantitative safety goals. The risk acceptability and cost-effectiveness of protective measures includes cost of the protective measures, attack probability,reduction in risk due to protective measures,probability of fatality conditional on successful terrorist attack and number of exposed individuals.

Container Cargo Simulation Modeling for Measuring Impacts of Infrastructure Investment Projects in Pearl River Delta

In the Pearl River Delta (PRD), there is severe competition between container ports, particularly those in Hong Kong, Shenzhen, and Guangzhou, for collecting international maritime container cargo. In addition, the second phase of the Nansha terminal in Guangzhou’s port and the first phase of the Da Chang Bay container terminal in Shenzhen opened last year. Under these circumstances, there is an increasing need to quantitatively measure the impact these infrastructure investments have on regional cargo flows. The analysis should include the effects of container terminal construction, berth deepening, and access road construction. The authors have been developing a model for international cargo simulation (MICS) which can simulate the movement of cargo. The volume of origin-destination (OD) container cargo in the East Asian region was used as an input, in order to evaluate the effects of international freight transportation policies. This paper focuses on the PRD area and, by incorporating a more detailed network, evaluates the impact of several infrastructure investment projects on freight movement.

Risk Assessment and Optimisation of Blast Mitigation Strategies for Design and Strengthening of Built Infrastructure

A probabilistic risk assessment procedure is developed which can predict risks of explosive blast damage to built infrastructure, and when combined with life-cycle cost analysis, the procedure can be used to optimise blast mitigation strategies. The paper focuses on window glazing since this is a load-capacity system which, when subjected to blast loading, has caused significant damage and injury to building occupants. Structural reliability techniques are used to derive blast reliability curves for annealed and toughened glazing subjected to explosive blast for a variety of threat scenarios. The probabilistic analyses include the uncertainties associated with blast modelling, glazing response and glazing failure criteria. Damage risks are calculated for an individual window and for windows in the facade of a multi-storey commercial building. The paper shows an illustrative example of how this information, when combined with risk-based decision-making criteria, can be used to optimise blast mitigation strategies.

The Impact of Transport Infrastructure and Pollutant Emissions in Sustainable Tourism for Mongolia

Tourism has a positive impact on economic growth,and it is one of the rapidly growing sectors in Mongolia.The Mongolian government,focusing on the development of tourism and transportation since 1990,has made it possible for achieving continuously growing sustainable tourism.Sustainable tourism is a way of maintaining a high level of tourist satisfaction while reducing adverse impacts on the environment.As transportation has been an integral part of the tourism industry,the purpose of this study is to examine the impact of transportation infrastructure,CO2 emission,and other classical demand factors on tourism flow in Mongolia by using a gravity model.Utilizing a panel data of tourists from 30 countries with the highest number of travel visits in Mongolia from 2002 to 2018,the study employs on panel co-integration analysis,aside from the conventional pooled ordinary least squares(OLS),fixed effects,and random effects estimators,to estimate the long-run relationship between Mongolian tourism flow and their respective determinants.According to the result of this study,the local transportation system and transportation investment have came out negative due to the underdeveloped transportation system.Moreover,the research indicates that carbon dioxide emission has a positive impact on tourism flow in the long-run.

Sustainable Development in the Context of Major Infrastructure Projects in United Kingdom

The present paper is aimed at reviewing sustainable development and sustainability approach for infrastructure projects in the United Kingdom. It is imperative that major infrastructure projects (MIPs) adhere to the principles of sustainable development in order to promote sustainability. This requires identifying sustainable strategies that are capable of serving as a guide to inculcating sustainability into major infrastructural projects. The current paper examines ways of inculcating sustainability into infrastructure projects bearing in mind that construction, maintenance and the way we use facilities have significant impacts on the environment. In addition to the fact that, decision making tools on methods of inculcating sustainability into infrastructure project appear too complex to stakeholders;and in most cases they do not provide stakeholders the necessary information required to make a good judgement. Hence, the present paper relies on desk study to gather existing data on infrastructure project and sustainable development. Existing data are obtained from books, scholarly articles and the WebPages of municipal authorities in the UK. Amongst other findings, the paper reveals that the utilization of environmental impact statements and environmental assessment documents at the formative stage of projects will aid the assessment of the level of sustainability to be achieved in any infrastructure development.

Financing Infrastructure still Faces Challenges

The inclusive growth and inter-connected development of the global economy is the highest concern of the G20 Summit.The rich experiences in the realm of infrastructure make me believe that increasing investment in high-quality infrastructure building will provide effective solutions to

What Are the Best Infrastructure Investments to Make？ Is It Based on Economics, or Resilience, or Both？

Our infrastructure investment decisions matter enormously if infrastructure is to be long lived, so how can we select infrastructure investments that are optimum？ How do we determine what would be the best investments to make.

Resilience of city underground infrastructure under multi-hazards impact:From structural level to network level

Underground infrastructure(UI)plays a great important role in the urbanization and modernization of megacities in the world.However,the intensive development of the UI during the past decades has posed great risks to the safety of city infrastructures under the impact of multi-hazards,especially with the condition of global climate change.In this paper,a general conceptualized framework to assess the resilience of UI in cities under multihazards impact is proposed.The urban tunnel system,e.g.,metro tunnel,road tunnel etc.,is selected as the typical underground infrastructure discussed with the emphasis both on the structural level in terms of mechanical behaviors and system level in terms of network efficiency.The hazards discussed in this paper include the natural hazards and human-related ones,with emphasis on earthquake,flood,and aggressive disturbances caused by human activities.After the general framework proposed for resilience of the structural and network behavior of the UI,two application examples are illustrated.The structural resilience of the shield tunnel under earthquake impact is analyzed by using the proposed resilience model,and the network resilience of the road tunnel system under the flood impact due to climate change is analyzed,respectively.The resilience enhancement by using the adaptive design strategy of real-time observational method is mathematically presented in this case.Some other practical engineering recovery measures are briefly discussed at the end of this application example.The findings in the application examples should be helpful to enhance the resilience-based design of the structural and network of tunnels from the component to the system level.

Expanding the SDI environment:comparing current spatial data infrastructure with emerging indoor location-based services

The authors compare key elements of the emerging field of Indoor Location-Based Services(Indoor LBS)to those currently found in spatial data infrastructure(SDI)programs.After a brief review of SDIs and Location-Based Services,the corresponding drivers,characteristics and emerging issues within the field of Indoor LBS are introduced and discussed.A comparative framework relates the two in terms of the criteria‘People’,‘Data’,‘Technologies’,‘Standards’and‘Policies/Institutional Arrangements’.After highlighting key similarities and differences,the authors suggested three areas–definition of common framework datasets in Indoor LBS,more effective use of volunteered geographic information by SDI programs and development of appropriate privacy policies by both communities–that may benefit from sharing‘lessons learned’.

Finance infrastructure through blockchain-based tokenization

The infrastructure finance gap has long-standing implications for economic and social development.Owing to low efficiency,high transaction costs,and long transaction time,conventional infrastructure financing instruments are considered to be major contributors to the increasing mismatch between the need for infrastructure development and available financing.Implemented through smart contracts,blockchain tokenization has shown characteristics that are poised to change the capital stack of infrastructure investment.This study analyzed the first SEC-compliant energy asset security token,Ziyen-Coin,from the perspective of the key participants,relevant regulations,and token offering procedures.Results show that tokenization can improve infrastructure assets liquidity,transaction efficiency,and transparency across intermediaries.Conventional infrastructure financing instruments were compared with blockchain tokenization by reviewing the literature on infrastructure finance.The benefits and barriers of tokenizing infrastructure assets were thoroughly discussed to devise ways of improving infrastructure financing.The study also found that the potential of tokenization has not yet been fully realized because of the limited technical infrastructures,regulation uncertainties,volatilities in the token market,and absence of the public sector.This study contributes to the present understanding of how blockchain technology can be implemented in infrastructure finance and the role of tokenization in the structure of public-private partnership and project finance.

Modeling the dynamic performance of transportation infrastructure using panel data model in state-space specifications

In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.

A Sustainable Funding and Financing Strategy for Urban Infrastructure Renewal in China

In a relatively short time, many of China's cities have become major industrial, shipping, and financial hubs. To support this unprecedented growth and economic development, China has invested enormous sums to provide transportation, power, communications, sanitation, and other basic infrastructures. Although much of this investment has been in newer urban districts, old districts within existing cities still add value to the economy and are often repositories of China's considerable cultural heritage. Maintaining compatibility between the old and the new is always challenging but the renewal of older infrastructure systems often lags behind due to a shortage of capital and difficulties in raising sufficient revenue to support replacement and upgrading of basic systems. This paper will examine the range of funding and financing options that are in use throughout the world to see what mix of public and private approaches might be most suitable for Chinese cities to adopt as part of a funding and financing strategy that will support enduring and sustainable renewal and redevelopment of older urban districts.

Characterization and Propagation of Historical and Projected Droughts in the Umatilla River Basin, Oregon, USA

Climate change is expected to have long-term impacts on drought and wildfire risks in Oregon as summers continue to become warmer and drier. This paper investigates the projected changes in drought characteristics and drought propagation in the Umatilla River Basin in northeastern Oregon for mid-century(2030–2059) and late-century(2070–2099) climate scenarios. Drought characteristics for projected climates were determined using downscaled CMIP5 climate datasets from ten climate models and Soil and Water Assessment Tool to simulate effects on hydrologic processes. Short-term(three months) drought characteristics(frequency, duration, and severity) were analyzed using four drought indices, including the Standardized Precipitation Index(SPI-3), Standardized Precipitation-Evapotranspiration Index(SPEI-3), Standardized Streamflow Index(SSI-3), and the Standardized Soil Moisture Index(SSMI-3). Results indicate that short-term meteorological droughts are projected to become more prevalent, with up to a 20% increase in the frequency of SPI-3drought events. Short-term hydrological droughts are projected to become more frequent(average increase of 11% in frequency of SSI-3 drought events), more severe, and longer in duration(average increase of 8% for short-term droughts).Similarly, short-term agricultural droughts are projected to become more frequent(average increase of 28% in frequency of SSMI-3 drought events) but slightly shorter in duration(average decrease of 4%) in the future. Historically, drought propagation time from meteorological to hydrological drought is shorter than from meteorological to agricultural drought in most sub-basins. For the projected climate scenarios, the decrease in drought propagation time will likely stress the timing and capacity of water supply in the basin for irrigation and other uses.

Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.