Case Studies of Failure, Damage Assessment, and Repair of Multispan Bridges in Argentina

Prato, Carlos A.; Gerbaudo, Carlos F.; Ceballos, Marcelo A.

doi:10.1061/40613(272)14

Cited by 1 publication

(5 citation statements)

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“…where Q n is the sum of artificial factors q i corresponding to n. When condition (8) was not met, the corresponding regions were excluded from the list of candidates.…”

Section: Choice Of Conditions For Classifying Regions As Non-priormentioning

confidence: 99%

“…The algorithm of the procedure for determining the number of candidate regions for participation in the distribution of state financial support can be represented by inequality (8).…”

Section: Tablementioning

confidence: 99%

“…The calculation results are presented in Table 5. Table 5 Checking the fulfilment of condition (8) Number Since condition (8) was not met with n=11, the calculations were terminated. Ten regions for state financial support have been identified as follows: Zhytomyr, Donetsk, Chernivtsi, Dnipropetrovsk, Vinnytsia, Cherkasy, Mykolayiv, Kyiv, Odesa and Kharkiv Oblasts.…”

Section: Tablementioning

confidence: 99%

“…For the countries of Latin America, in particular Brazil, the problem of unsuccessful bridges and roads is also relevant [8]. This fact is recorded in the work [9], where attention is focused on the age of the bridges.…”

Section: Introductionmentioning

confidence: 99%

“…Financing bridge construction is a challenging task [15]. These questions were raised in the first half of the 20 th century [16] and are relevant in the 21st century [8,[17][18][19][20]. Summing up, on the one hand, we have a catastrophic wear and tear on bridges; on the other hand, there are backward regions that are unable to cope with the problem of bridge wear.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of state financial support for non-priority territorial units using the example of bridge construction

Левченко

Брітченко²

2021

EEJET

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The article discloses the problem of distributing state financial support based on an integrated approach. The study has proved the urgency and necessity of state support for the lowest priority territorial units (regions). It answers the research question of what components need to be included in the methodology for determining state financial support. A comprehensive method for estimating the share of public funds is proposed, taking into account the investment attractiveness of a region (oblast) and the risk of the corresponding region (oblast). To achieve this goal, the following general scientific and special methods and research techniques were used in the work, such as comparative analysis of scientific literature and information sources based on methods of comparison, systematization, and generalization; generalization of the analysis results, as well as logical generation of conclusions and integral assessment. Since the problem of financing the construction and reconstruction of bridges is relevant for a number of countries, this technique was tested using an example of bridge construction. According to the obtained results, territorial units (regions) that are not leaders in priority for the investor and have a high level of riskiness of investing financial resources become eligible for state financial support. The problem of financing such regions can be solved only through state support. The results of calculations show that the distribution of financial resources with the available volume of public finances K=1 allocated for support is carried out proportionally. An integrated approach made it possible to identify 10 territorial units (oblasts) for funding, with the oblasts with the worst priority factors receiving the largest share of state financial support. This study is of practical interest to government agencies in the distribution of public funds, and it is of theoretical importance to researchers dealing with issues of financial security and public administration

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“…where Q n is the sum of artificial factors q i corresponding to n. When condition (8) was not met, the corresponding regions were excluded from the list of candidates.…”

Section: Choice Of Conditions For Classifying Regions As Non-priormentioning

confidence: 99%

“…The algorithm of the procedure for determining the number of candidate regions for participation in the distribution of state financial support can be represented by inequality (8).…”

Section: Tablementioning

confidence: 99%