{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5F3ERDVEMLXQ2HMW5WS4GSYZDB","short_pith_number":"pith:5F3ERDVE","schema_version":"1.0","canonical_sha256":"e976488ea462ef0d1d96eda5c34b1918793984b3b384f827ed572751f3ac972c","source":{"kind":"arxiv","id":"1902.05391","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Bridge Load Capacity Estimation in Post-Disaster and -Conflict Zones","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Ahmed Soliman Khaled, Arya Pamuncak, Irwanda Laory, Weisi Guo","submitted_at":"2019-02-05T23:44:17Z","abstract_excerpt":"Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance and prevent failure. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as method"},"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":"1902.05391","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-05T23:44:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"3151a179fc94df75b16803e9869f12ee248ba0d27f071f3ecb00d29d1b69778c","abstract_canon_sha256":"beba5ad01929cfa5fce966440c48fa98b3cd2f1ac0020057c2d9a0e4fca2625c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:00.948017Z","signature_b64":"ex0lMkpww/MYZ2wntF50tZ4ifj30CSVuRAb00uzBwbd7jm6iU4dbWPt2vGE0wrvCtvVUbcqGsMecEgKjY+N/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e976488ea462ef0d1d96eda5c34b1918793984b3b384f827ed572751f3ac972c","last_reissued_at":"2026-05-17T23:54:00.947310Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:00.947310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Bridge Load Capacity Estimation in Post-Disaster and -Conflict Zones","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Ahmed Soliman Khaled, Arya Pamuncak, Irwanda Laory, Weisi Guo","submitted_at":"2019-02-05T23:44:17Z","abstract_excerpt":"Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance and prevent failure. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.05391","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":""},"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":"1902.05391","created_at":"2026-05-17T23:54:00.947427+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.05391v1","created_at":"2026-05-17T23:54:00.947427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.05391","created_at":"2026-05-17T23:54:00.947427+00:00"},{"alias_kind":"pith_short_12","alias_value":"5F3ERDVEMLXQ","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5F3ERDVEMLXQ2HMW","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5F3ERDVE","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB","json":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB.json","graph_json":"https://pith.science/api/pith-number/5F3ERDVEMLXQ2HMW5WS4GSYZDB/graph.json","events_json":"https://pith.science/api/pith-number/5F3ERDVEMLXQ2HMW5WS4GSYZDB/events.json","paper":"https://pith.science/paper/5F3ERDVE"},"agent_actions":{"view_html":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB","download_json":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB.json","view_paper":"https://pith.science/paper/5F3ERDVE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.05391&json=true","fetch_graph":"https://pith.science/api/pith-number/5F3ERDVEMLXQ2HMW5WS4GSYZDB/graph.json","fetch_events":"https://pith.science/api/pith-number/5F3ERDVEMLXQ2HMW5WS4GSYZDB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB/action/storage_attestation","attest_author":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB/action/author_attestation","sign_citation":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB/action/citation_signature","submit_replication":"https://pith.science/pith/5F3ERDVEMLXQ2HMW5WS4GSYZDB/action/replication_record"}},"created_at":"2026-05-17T23:54:00.947427+00:00","updated_at":"2026-05-17T23:54:00.947427+00:00"}