{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LF46FJWRLJQQDICNS2VVWRRMFY","short_pith_number":"pith:LF46FJWR","schema_version":"1.0","canonical_sha256":"5979e2a6d15a6101a04d96ab5b462c2e18cab07122dcd13aa41bbeaee247586f","source":{"kind":"arxiv","id":"2106.14841","version":1},"attestation_state":"computed","paper":{"title":"Gaussian Process Regression for Active Sensing Probabilistic Structural Health Monitoring: Experimental Assessment Across Multiple Damage and Loading Scenarios","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"eess.SP","authors_text":"Ahmad Amer, Fotis Kopsaftopoulos","submitted_at":"2021-06-28T16:38:09Z","abstract_excerpt":"In the near future, Structural Health Monitoring (SHM) technologies will be capable of overcoming the drawbacks in the current maintenance and life-cycle management paradigms, namely: cost, increased downtime, less-than-optimal safety management paradigm and the limited applicability of fully-autonomous operations. In the context of SHM, one of the most challenging tasks is damage quantification. Current methods face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current frameworks does not offer "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.14841","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2021-06-28T16:38:09Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"cc07e74139a687d336e171e5afcf21ec8c49fc788d2a043cf0e2417bac4996b6","abstract_canon_sha256":"7e1763b6862e312dc8a7429fe6cd32cb506280cbe5eeb70dc98a95341a20a514"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:24.489343Z","signature_b64":"jNXMraRwGJoU43q5wxDHL+mHSE+guErZ4bRkFKsWNXrm2cdCxNMP9FT0TUEEApwBvVarXrmyyLSidDmF8QkMAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5979e2a6d15a6101a04d96ab5b462c2e18cab07122dcd13aa41bbeaee247586f","last_reissued_at":"2026-07-05T10:57:24.488877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:24.488877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Process Regression for Active Sensing Probabilistic Structural Health Monitoring: Experimental Assessment Across Multiple Damage and Loading Scenarios","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"eess.SP","authors_text":"Ahmad Amer, Fotis Kopsaftopoulos","submitted_at":"2021-06-28T16:38:09Z","abstract_excerpt":"In the near future, Structural Health Monitoring (SHM) technologies will be capable of overcoming the drawbacks in the current maintenance and life-cycle management paradigms, namely: cost, increased downtime, less-than-optimal safety management paradigm and the limited applicability of fully-autonomous operations. In the context of SHM, one of the most challenging tasks is damage quantification. Current methods face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current frameworks does not offer "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.14841","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.14841/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.14841","created_at":"2026-07-05T10:57:24.488930+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.14841v1","created_at":"2026-07-05T10:57:24.488930+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.14841","created_at":"2026-07-05T10:57:24.488930+00:00"},{"alias_kind":"pith_short_12","alias_value":"LF46FJWRLJQQ","created_at":"2026-07-05T10:57:24.488930+00:00"},{"alias_kind":"pith_short_16","alias_value":"LF46FJWRLJQQDICN","created_at":"2026-07-05T10:57:24.488930+00:00"},{"alias_kind":"pith_short_8","alias_value":"LF46FJWR","created_at":"2026-07-05T10:57:24.488930+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.01666","citing_title":"Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY","json":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY.json","graph_json":"https://pith.science/api/pith-number/LF46FJWRLJQQDICNS2VVWRRMFY/graph.json","events_json":"https://pith.science/api/pith-number/LF46FJWRLJQQDICNS2VVWRRMFY/events.json","paper":"https://pith.science/paper/LF46FJWR"},"agent_actions":{"view_html":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY","download_json":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY.json","view_paper":"https://pith.science/paper/LF46FJWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.14841&json=true","fetch_graph":"https://pith.science/api/pith-number/LF46FJWRLJQQDICNS2VVWRRMFY/graph.json","fetch_events":"https://pith.science/api/pith-number/LF46FJWRLJQQDICNS2VVWRRMFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY/action/storage_attestation","attest_author":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY/action/author_attestation","sign_citation":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY/action/citation_signature","submit_replication":"https://pith.science/pith/LF46FJWRLJQQDICNS2VVWRRMFY/action/replication_record"}},"created_at":"2026-07-05T10:57:24.488930+00:00","updated_at":"2026-07-05T10:57:24.488930+00:00"}