How AI/Machine Learning and a GIS CMMS Can Meet Performance Based GASB 34 Modified Approach Accounting Compliance for Water Pipes

摘要

The Governmental Accounting Standards Board (GASB) is the authoritative source of GAAP for state and local governments. In 1999, GASB released a significant pronouncement entitled GASB Statement #34, which provided an overhaul of the manner by which financial reporting should occur for government. It provided a comprehensive framework for financial reporting, with the intention of making annual reports easier to understand. The most notable aspect of GASB 34 was that, for the first time, general infrastructure assets were to be reported together with related depreciation or preservation costs. In 2005, the Governmental Accounting Standards Board (GASB), through GASB 34, required all governmental entities reporting their annual financial information to report capital assets on the statement of financial position. During this time many engineering consulting firms believed that GASB 34 would force local governments to engage in infrastructure asset management planning practices with the requirements of measuring the condition and deterioration of long-lived infrastructure assets such as roadways, bridges, sidewalks, water, and sewer lines. That solution was the modified approach for reporting infrastructure assets. Local accounting departments and audit firms facing all of the changes of the annual financial statements lacked an asset inventory with installed dates and valuations and without any training in infrastructure asset management and resorted to an inaccurate aged-based service life depreciation approach to reporting their linear capital assets. Some accounting departments did adopt the modified approach for street networks which could be visibly inspected in order to report on an assessed condition to benchmark against an agreed upon service level. Long term, essentially inexhaustible sustainable infrastructure systems like underground utility networks lacked a cost-effective technology to also apply the condition assessment based modified approach to reporting on this critical infrastructure network. This has also been a default criterion to measure for the ASCE Infrastructure Report Card efforts for drinking water systems. Since buried infrastructure primarily consists of pipe which has no moving parts and is not readily accessible, performance-based management of these buried assets has historically not been performed in the water industry. By applying new Artificial Intelligence Machine Learning condition assessment to underground water pipes the standards of pipe performance evaluation can be established and used in GASB 34's modified approach for accounting compliance at a minimum of every three years. This utility engineering effort greatly reduces the time required by finance and accounting to report on buried infrastructure systems. This approach combines utility engineering with a GIS-centric asset location and geo-database storage, AI/machine learning as the cost-effective system-wide condition assessment technology, and a GIS-centric CMMS to conduct on-going work order maintenance and direct inspections to create a powerful asset management solution with the ability to align to finance department budgeting and financial reporting requirements.